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NATIONAL

COOPERATIVE

HIGHWAY RESEARCH

PROGRAMNCHRPSYNTHESIS 364

Estimating Toll Road

Demand and Revenue

A Synthesis of Highway Practice



TRANSPORTATION RESEARCH BOARD 2007 EXECUTIVE COMMITTEE*

OFFICERS

Chair: Linda S. Watson, CEO, LYNX–Central Florida Regional Transportation Authority, Orlando

Vice Chair: Carol A. Murray, Commissioner, New Hampshire DOT, Concord

Executive Director: Robert E. Skinner, Jr., Transportation Research Board

MEMBERS

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LARRY L. BROWN, SR., Executive Director, Mississippi DOT, Jackson

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NICHOLAS J. GARBER, Henry L. Kinnier Professor, Department of Civil Engineering, University of Virginia, Charlottesville

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ADIB K. KANAFANI, Cahill Professor of Civil Engineering, University of California, Berkeley

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MICHAEL D. MEYER, Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta

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JOHN R. NJORD, Executive Director, Utah DOT, Salt Lake City

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SANDRA ROSENBLOOM, Professor of Planning, University of Arizona, Tucson

TRACY L. ROSSER, Vice President, Corporate Traffic, Wal-Mart Stores, Inc., Bentonville, AR

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HENRY G. (GERRY) SCHWARTZ, JR., Senior Professor, Washington University, St. Louis, MO

C. MICHAEL WALTON, Ernest H. Cockrell Centennial Chair in Engineering, University of Texas, Austin

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EX OFFICIO MEMBERS

THAD ALLEN (Adm., U.S. Coast Guard), Commandant, U.S. Coast Guard, Washington, DC

THOMAS J. BARRETT (Vice Adm., U.S. Coast Guard, ret.), Pipeline and Hazardous Materials Safety Administrator, U.S.DOT

MARION C. BLAKEY, Federal Aviation Administrator, U.S.DOT JOSEPH H. BOARDMAN, Federal Railroad Administrator, U.S.DOT

JOHN A. BOBO, JR., Acting Administrator, Research and Innovative Technology Administration, U.S.DOT

REBECCA M. BREWSTER, President and COO, American Transportation Research Institute, Smyrna, GA

GEORGE BUGLIARELLO, Chancellor, Polytechnic University of New York, Brooklyn, and Foreign Secretary, National Academy

of Engineering, Washington, DC

J. RICHARD CAPKA, Federal Highway Administrator, U.S.DOT

SEAN T. CONNAUGHTON, Maritime Administrator, U.S.DOT

EDWARD R. HAMBERGER, President and CEO, Association of American Railroads, Washington, DC

JOHN H. HILL, Federal Motor Carrier Safety Administrator, U.S.DOT

JOHN C. HORSLEY, Executive Director, American Association of State Highway and Transportation Officials, Washington, DC

J. EDWARD JOHNSON, Director, Applied Science Directorate, National Aeronautics and Space Administration, John C. Stennis

Space Center, MS

WILLIAM W. MILLAR, President, American Public Transportation Association, Washington, DC

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JEFFREY N. SHANE, Under Secretary for Policy, U.S.DOT

JAMES S. SIMPSON, Federal Transit Administrator, U.S.DOT

CARL A. STROCK (Lt. Gen., U.S. Army), Chief of Engineers and Commanding General, U.S. Army Corps of Engineers, Washington, DC

*Membership as of January 2007.



TRANSPORTATION RESEARCH BOARD

WASHINGTON, D.C.

2006

www.TRB.org

NAT IONAL COOPERAT IVE H IGHWAY RESEARCH PROGRAM

NCHRP SYNTHESIS 364

Research Sponsored by the American Association of State Highway and Transportation Officials

in Cooperation with the Federal Highway Administration

SUBJECT AREAS

Planning and Administration

Estimating Toll Road Demand and Revenue

A Synthesis of Highway Practice

CONSULTANTS

DAVID KRIGER

SUZETTE SHIU

and

SASHA NAYLOR

iTRANS Consulting

Richmond Hill, ON, Canada



NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM

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Project 20-5 (Topic 36-11)

ISSN 0547-5570

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© 2006 Transportation Research Board

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NCHRP COMMITTEE FOR PROJECT 20-5

CHAIR

GARY D. TAYLOR, CTE Engineers

MEMBERS

THOMAS R. BOHUSLAV, Texas DOT

DONN E. HANCHER, University of Kentucky

DWIGHT HORNE, Federal Highway Administration

YSELA LLORT, Florida DOT

WESLEY S.C. LUM, California DOT JAMES W. MARCH, Federal Highway Administration

JOHN M. MASON, JR., Pennsylvania State University

CATHERINE NELSON, Oregon DOT

LARRY VELASQUEZ, New Mexico DOT

PAUL T. WELLS, New York State DOT

FHWA LIAISON

WILLIAM ZACCAGNINO

TRB LIAISON

STEPHEN F. MAHER

COOPERATIVE RESEARCH PROGRAMS STAFFROBERT J. REILLY, Director, Cooperative Research Programs

CRAWFORD F. JENCKS, Manager, NCHRP

EILEEN DELANEY, Director of Publications

NCHRP SYNTHESIS STAFF

STEPHEN R. GODWIN, Director for Studies and Special Programs

JON WILLIAMS, Manager, Synthesis Studies

GAIL STABA, Senior Program Officer

DONNA L. VLASAK, Senior Program Officer

DON TIPPMAN, Editor CHERYL KEITH, Senior Program Assistant

TOPIC PANEL

DEBORAH BROWN, Virginia Department of Transportation

MARK W. BURRIS, Texas A&M University

KIMBERLY FISHER, Transportation Research Board

DAVID FORTE, Washington State Department of Transportation

CHERIAN GEORGE, Fitch Ratings, New York

JOSEPH M. GIGLIO, Northeastern University

JOEY GORDON, Florida Department of Transportation

DIANA VARGAS, Texas Department of Transportation

PATRICK T. DeCORLA-SOUZA, Federal Highway Administration

(Liaison)

SUZANNE SALE, Federal Highway Administration (Liaison)



Highway administrators, engineers, and researchers often face problems for which infor-

mation already exists, either in documented form or as undocumented experience and prac-

tice. This information may be fragmented, scattered, and unevaluated. As a consequence,

full knowledge of what has been learned about a problem may not be brought to bear on its

solution. Costly research findings may go unused, valuable experience may be overlooked,

and due consideration may not be given to recommended practices for solving or alleviat-

ing the problem.

There is information on nearly every subject of concern to highway administrators and

engineers. Much of it derives from research or from the work of practitioners faced with

problems in their day-to-day work. To provide a systematic means for assembling and eval-

uating such useful information and to make it available to the entire highway community,

the American Association of State Highway and Transportation Officials—through the

mechanism of the National Cooperative Highway Research Program—authorized the

Transportation Research Board to undertake a continuing study. This study, NCHRP Proj-

ect 20-5, “Synthesis of Information Related to Highway Problems,” searches out and syn-

thesizes useful knowledge from all available sources and prepares concise, documented

reports on specific topics. Reports from this endeavor constitute an NCHRP report series,Synthesis of Highway Practice.

This synthesis series reports on current knowledge and practice, in a compact format,

without the detailed directions usually found in handbooks or design manuals. Each report

in the series provides a compendium of the best knowledge available on those measures

found to be the most successful in resolving specific problems.

FOREWORD By Staff

Transportation

Research Board

This synthesis reports on the state of the practice for forecasting demand and revenues

for toll roads in the United States. The synthesis focused on the models that are used to

forecast the demand for travel. It also considered the application of these models to pro-

ject revenues as a function of demand estimates. Goals included: developing a profile of

the current state of the practice in toll road demand forecasting; identifying technical mod-

eling issues that affect the accuracy, effectiveness, and reliability of the forecasts; and mak-

ing recommendations for research to improve the state of the practice. The report is

intended to serve as a resource for state departments of transportation (DOTs), metropoli-

tan planning organizations, tolling authorities and operators, potential investors, bond rat-

ing agencies, and consultants who prepare models and forecasts on behalf of DOTs and

other toll facility owners.

A survey was distributed to various state DOTs, toll authorities, bond rating agencies,

and bond insurance agencies in the United States. A literature search was undertaken to

identify relevant research reports, papers, and other publications for review.

David Kriger, Suzette Shiu, and Sasha Naylor, iTRANS Consulting, Richmond Hill, ON,

Canada, collected and synthesized the information and wrote the report. The members of

the topic panel are acknowledged on the preceding page. This synthesis is an immediatelyuseful document that records the practices that were acceptable within the limitations of the

knowledge available at the time of its preparation. As progress in research and practice con-

tinues, new knowledge will be added to that now at hand.

PREFACE



CONTENTS

1 SUMMARY

3 CHAPTER ONE INTRODUCTION

Context, 3

Purpose and Scope, 4

Organization, 5

Audiences, 5

Definitions, 6

8 CHAPTER TWO METHOD FOR LITERATURE REVIEW AND SURVEY

Literature Review, 8

Survey of Practitioners, 8

10 CHAPTER THREE TOLL ROAD FORECASTING: STATE OF THE PRACTICE

Travel Demand Forecasting Models, Applications, and Evolution, 10

Relationship Between Demand and Revenue Forecasts, 19

Performance of Toll Road Demand and Revenue Forecasts, 19

Treatment of Specific Factors Affecting Forecast Performance, 25

36 CHAPTER FOUR CHECKLISTS AND GUIDELINES TO IMPROVE PRACTICE

Checklists and Guidelines for Models, 36

Guidelines Specific to Toll Road Feasibility Studies, 37

Traffic Risk Index, 38

Summary Observations, 38

40 CHAPTER FIVE CONCLUSIONS

43 REFERENCES

46 BIBLIOGRAPHY

49 GLOSSARY

51 APPENDIX A DEVELOPMENT AND ADMINISTRATION OF SURVEY

54 APPENDIX B SURVEY QUESTIONNAIRE

81 APPENDIX C SUMMARY OF SURVEY RESPONSES

103 APPENDIX D CHARACTERISTICS OF RECORDED TOLL ROADS



Throughout the United States, traditional public-sector funding sources for transportation

projects are becoming less able to meet the growing demand for highway infrastructure. The

shortfall affects the construction of the highway, as well as its operation, maintenance, and

expansion. As a result, state departments of transportation are increasingly seeking alterna-

tive methods, such as toll facilities, of financing for new highway projects, with a greater

reliance on private-sector sources.

For toll facilities to be financially viable and/or attractive to potential investors (public–

private partnerships, etc.) in the future, the facility must be seen to be able to generate suffi-cient revenue from operations to cover debt service cost, and potentially other project and

maintenance costs over the lifetime of the facility. This requires a reliable and credible fore-

cast of the expected revenues, which are functions of the estimated traffic demand and toll

rates for the facility. However, in the past, industry experience in the toll demand forecasts

upon which these are based have been quite varied, in that demand (and the accompanying

revenues) has ranged from overestimated in many cases to occasionally underestimated. In

addition, the accuracy of when specific levels of demand are projected to occur has been

mixed, with problems being particularly acute in the short-term facility ramp-up period. The

resultant variations have had significant impacts on both the actual revenue streams and on

the facility’s debt structuring and obligations. This has led to concerns among facility own-

ers and the financial community (which rates and insures and/or invests the bonds that are

issued for the facility’s implementation) about the accuracy, reliability, and effectiveness of

the demand forecasts upon which the revenue projections are based.

The purpose of this synthesis is to report the state of the practice in demand forecasting

models that are used as the basis of revenue forecasts for toll roads in the United States. The

synthesis profiles the current state of the practice in toll road demand and revenue forecast-

ing, through a literature review and a survey of practitioners. It identifies the technical

modeling issues that affect the performance of the forecasts and how these have been treated

in current and emerging practice. The synthesis also develops checklists and lists of ques-

tions that practitioners (owners, proponents, and financial backers) can use to improve the

state of the practice. Finally, recommendations for future areas of research are identified. The

synthesis focuses on the state of the practice in the United States; however, it also references

international practices where applicable.

A total of 138 survey questionnaires were distributed to either state departments of trans-

portation, toll authorities, bond rating agencies, and bond insurance agencies. Responses

were received from 55 agencies (40%), of whom 25 completed the first two parts and 13 the

entire questionnaire.

SUMMARY

ESTIMATING TOLL ROAD

DEMAND AND REVENUE



3

CONTEXT

Throughout the United States, traditional public-sector fund-

ing sources for transportation projects are unable to meet the

growing demand for new highway infrastructure and maintain

an aging infrastructure. Motor fuel taxes—the primary source

of transportation finance in the United States—have not kept

pace with the demand for travel and, in turn, for capital invest-

ment, owing to inflation, improved fuel efficiency, and

increased vehicle usage (1). The shortfall can lead to economicimpacts on highway construction, as well as its operation,

maintenance, and expansion. As a result, some state depart-

ments of transportation (DOTs) and transportation authorities

are relying increasingly on tolling as an alternative means of

financing new and expanded highway infrastructure. A related

problem is the need to improve the management and utiliza-

tion of existing facilities, because many of the urban centers

that require additional capacity to relieve congestion have lim-

ited space for expansion.

With budget shortfalls and an increasing demand for road-

way facilities to alleviate the congestion problems that are

common to many highways, state DOTs are turning to user-based fees or tolling as a means of financing roadway

improvements and expansion and managing growing traffic

demand for both interurban and urban facilities. The Texas

DOT, for example, has determined that any new highway

project in the state must be evaluated as a toll road (2).

As state DOTs turn to tolling, increasing attention is being

focused on the performance of the underlying revenue fore-

casts and the projected ability of the facility to service debt.

This is because the performance of the revenue forecasts,

which are derived largely from forecasts of traffic demand,

has varied among projects. In some cases, the lower-than-

anticipated revenues were addressed through alternative

sources of revenue to pay debt service, such as other toll roads,

gas taxes, or government guarantees, or, where available,

through sufficient reserve funds. This ensured that no strug-

gling project was entirely dependent on traffic revenues for

debt payment. As a result, between 1985 and 1995, forecast-

ing errors or inaccuracies, which were known to exist, did not

result in a single default in payment or any serious payment

difficulties with new toll road projects in the United States (3).

However, concern has since been expressed that these

alternate sources of revenue may not be available to protect

more recent projects or even future projects. For example, the

privately held Dulles Greenway in Virginia went into default

in 1996, as a result of toll revenues being less than projected

(achieving only 20% of projected revenues in 1995, its first

year of operation, and still only 35% of projected revenues in

its fifth year). Other toll roads have also struggled; for exam-

ple, revenues from the Southern Connector in South Carolina

have been sufficient to cover operating costs but only a por-

tion of the debt service, because traffic projections have not

been met (just over half of the projected demand was realizedin its third year of operation). Similarly, traffic on the Poca-

hontas Parkway located southeast of Richmond, Virginia, has

been just under half of the projected demand in its second year

of operation. One result is that the credit ratings for the bonds

for both facilities were lowered (4). The Foothill/Eastern toll

road in Orange County, California, was refinanced in 1999 (3).

Contributing factors to the financial problems of the

Pocahontas Parkway and the Foothill/Eastern toll roads (and

others) have been attributed to various inaccuracies in the

demand and revenue forecasts, which included the unantici-

pated affects of a recession, actual ramp-up volumes being

less than projected, and the expected extension of a connect-

ing road not occurring (5).

For toll facilities to be financially viable and/or attractive

to potential investors (public–private partnerships, etc.) in the

future, the facility must be seen to be able to generate suffi-

cient revenue from operations to cover debt service cost and

potentially other project and maintenance costs over the life-

time of the facility, as well as providing a reasonable return

on equity. This requires a reliable and credible forecast of the

expected revenues, which are functions of the estimated traf-

fic demand and toll rates for the facility. However, industry

experience in tolling forecasts and the associated recoverable

benefits historically have been quite varied, in that demand

(and the accompanying revenues) has ranged from frequently

overestimated to occasionally underestimated. Also, the accu-

racy of when specific levels of demand are projected to occur

has been mixed, with problems being particularly acute in the

short-term facility ramp-up. The resultant variations have had

significant impacts on both the actual revenue streams and on

the facility’s debt structuring and obligations. This has led to

concerns among facility owners and the financial community

(which rates and insures and/or invests the bonds that are

issued for the facility’s implementation) about the accuracy,

reliability, and effectiveness of the demand forecasts upon

which the revenue projections are based. In addition, the

CHAPTER ONE

INTRODUCTION



growing use of Intelligent Transportation Systems and other

technologies provides DOTs with the ability to implement

variable pricing, HOT (high-occupancy toll) lanes, and other

innovations that require a greater level of accuracy in the

depiction of demand and revenues.

It is of critical importance that the forecasted tollrevenue targets be accurate, based on the ability of the toll

road to achieve its forecasted traffic targets, if debt service

on bonds or a return on equity for private operators are to

be paid (6 ). The fiscal feasibility of new toll road projects,

or the revenue side of the benefit–cost relationship, is based

on models that forecast traffic demand. Therefore, the reli-

ability, accuracy, and effectiveness of these forecasts are

important, because they are critical in determining the

credit quality of the projects.

Accordingly, to maximize the prospects of a project’s

financial viability—that is, the likelihood that the forecasts

match the actual revenues—it is necessary to look at the earlystages of the process; namely, the models that are used to

forecast travel (traffic) demand and their resulting successes

or failures to improve the forecast results. To understand the

reasons for unrealized traffic demand forecasts, it is neces-

sary to review not only the model as a whole, but to separate

it into its constituent structure, inputs, calibration, and appli-

cation and examine the underlying assumptions that drive the

forecasts. This synthesis explores the subject in a manner that

takes into account viewpoints and experiences from both the

engineering and planning communities and the financial

community.

PURPOSE AND SCOPE

Purpose

The purpose of this synthesis is to report the state of the prac-

tice in toll road demand forecasting models that are used as

the basis of revenue forecasts for these facilities. The syn-

thesis had four specific goals:

• Develop a profile of the current state of the practice in

toll road demand forecasting through a survey of the

forecasting community and a literature review.

• Based on this profile, identify the technical modelingissues that affect the accuracy, effectiveness, and relia-

bility of the forecasts.

• Make recommendations regarding ways to improve the

state of the practice.

• Identify areas for potential future research.

Subject

This synthesis focused on the models that are used to fore-

cast the demand for travel on tolled facilities and their reli-

ability. It also took into account the application of these

4

models to project revenues as a function of the demand

estimates. Several different aspects of the models were

considered.

• Purposes of toll demand forecasting (i.e., how they dif-

fer from other travel demand forecasts).

• Methods for forecasting toll facility demand and rev-

enues, ranging from simple sketch-planning tools to

investment grade studies; also taking into account how

these have changed over time.

• Products provided from forecasting, and how they were

used.

• Input data used, such as surveys, and data sources and

key assumptions.

• Methods of estimating the value of time.

• Treatment of different horizon years, with particular

attention to short-term (ramp-up) forecasts.

• Peer review procedures.

• Comparison of forecasts with the actual experience.

• Comparison of the accuracy and effectiveness of varioustoll facility demand and revenue forecasting methods.

• Variables associated with forecasting successes and

failures, such as the type of proposed investment (i.e.,

stand-alone projects or expansions to existing projects),

financing methods, modeling techniques, etc.

• Assessment of risk for demand and revenue forecasts.

• Transparency (i.e., availability and understanding of

detailed information on modeling procedures, the

underlying assumptions, etc.).

• Innovative techniques used to improve the quality of

forecasts.

Scope

Given the subject, the scope of what was included in this syn-

thesis is defined by the following:

• Tolled roads and highways and related infrastructure,

including bridges and tunnels. In practice, these facili-

ties generally have limited access; that is, they gener-

ally are not accessible from adjacent properties.

• HOT lanes (i.e., limited access lanes that provide free or

reduced cost access to qualifying high-occupancy vehi-

cles (HOV), but also provide access to other paying

vehicles that do not meet the occupancy requirement).

• Road-based traffic forecasts for the aforementioned

facilities; typically light vehicles (i.e., personal auto-

mobiles, vans, pick-up trucks, or motorcycles) and

heavy vehicles (typically trucks), but potentially

including commercial, fleet, or service vehicles and

public transit buses (i.e., road-based transit, but not

including rail that operates in mixed traffic).

• Urban as well as interurban tolled facilities.

• Different types of facility ownership.

• Different types of toll collection, pricing schemes, level

of service and capacity differences, etc. Distinctions were

noted where they were appropriate to the forecasting



5

process (e.g., the incorporation of bias factors regarding

the type of toll collection).

• Toll roads and highways anywhere in the United States,

although experiences elsewhere could be considered if

they were deemed relevant to the situation in the United

States.

• Different types of proposed investments, ranging fromnetwork-wide feasibility planning studies to facility-

specific forecasts.

• Forecasts up to 30 years old (although virtually the

entire literature addressed facilities that were 20 or

fewer years old), with a focus on the most recent expe-

rience to appropriately represent the prime objective of

the state of the practice.

The following topics were beyond the scope of the syn-

thesis and so were not included:

• Non-tolled roads and highways of any kind.

• Transit-only infrastructure or facilities for other modesof transportation.

Study Process

The synthesis focused on the practice as opposed to aca-

demic or theoretical considerations, although the latter were

noted if they were relevant. The synthesis is based on infor-

mation that was acquired either through a review of the liter-

ature or by means of a web-based survey of state DOTs, toll

authorities and their consultants, bond rating agencies, and

bond insurance agencies. Thus, the synthesis considered the

perspectives of both the engineering and planning communi-ties (the builders and owners) and the financial community

(the financial backers), assembling factual information as

well as opinions and interpretations.

Where appropriate, information from actual cases was

used to illustrate the different approaches: these were drawn

from the literature or the survey.

Finally, it is important to note that this synthesis focused

on the technical aspects of the state of the practice in model-

ing. It was not intended to be judgmental with respect to the

policies of individual states, tolling authorities, bond rating

agencies, or any other parties, or how these organizationshave used the resultant forecasts.

ORGANIZATION

This synthesis is organized into five chapters. The remainder

of chapter one describes the balance of the report, identifies

the intended audiences for the synthesis, and presents vari-

ous definitions for terms that are used in the text.

Chapter two describes the methods used for the litera-

ture review and for a survey of practitioners. This chapter

is not intended to be a detailed discussion, but rather pre-

sents an understanding of how the information was gath-

ered and any caveats or comments that may be associated

with the information.

Chapter three is the core of the synthesis. It begins by

describing the state of the practice and the emerging devel-opments in travel demand forecasting, and then describes the

applications to toll road traffic and revenue forecasts. The

chapter then describes the problem at hand by documenting

and commenting on the “performance” of toll facility fore-

casts and the reasons for this performance. The discussion is

based on the information collected in the literature search and

in the survey of state DOTs, toll authorities, bond rating

agencies, and bond insurance agencies. Although the state of

the practice largely reflects that of the United States, prac-

tices elsewhere also are considered, where appropriate.

Taking into account the identified problems and how oth-

ers have addressed them, chapter four synthesizes the state of the practice in terms of “checklists” and indices that practi-

tioners can use to identify and account for the relevant issues

and needs when they develop and apply travel demand mod-

els for toll road traffic and revenue forecasts.

Chapter five offers conclusions drawn from the findings

and makes suggestions for future research in the area of toll

road demand and revenue forecasting. A list of references, a

bibliography of sources, and a glossary of selected terms are

also included.

Four appendixes complement the synthesis. Appendix A

describes the development and administration of the survey,as well as the response to the survey. Appendix B is a copy

of the original survey as sent to practitioners. Appendix C

tabulates the survey results. Appendix D describes the char-

acteristics of the toll facilities that were included in the chap-

ter three comparison of projected and actual revenues.

AUDIENCES

This synthesis is intended to serve as a resource for several

types of organizations.

• State DOTs, which are in various stages of considering,

planning, implementing, and operating tolled facilities,

either within the organization specifically, through a

dedicated authority, and/or some type of arrangement

with a private-sector owner. The DOTs also set policies

(pricing, funding, enforcement, etc.) and are responsible

for other competing or complementary transportation

infrastructure.

• Metropolitan planning organizations (MPOs), which

are responsible for planning an urban area’s long-range

transportation plan, within which a tolled facility must

be planned, and for gaining a consensus on priorities for



funding and implementation (if federally funded proj-

ects; not required for privately funded).

• Tolling authorities and operators, who are charged with

the actual planning, implementation, financing, opera-

tion, maintenance, and expansion of a specific facility.

• Potential investors, who might provide the financial

backing for the facility.• Bond rating agencies, which must assess the underlying

credit quality of the bonds.

• Bond insurance agencies, which underwrite the bonds.

• Consultants, who prepare the models and the forecasts

on behalf of the DOTs and facility owners.

• Academia and researchers, who might seek ways to

improve the modeling process, algorithms, structure,

inputs (e.g., through improved quality control or risk

management), and the application of the actual model.

Although it is based largely on practice in the United

States, it is expected that the synthesis also would be of inter-

est to audiences outside the country for adoption to localneeds, and because of the growing involvement of foreign

financiers (and their consultants) in owning and operating

facilities in the United States. Finally, it is important to note

that the broad list of audiences in the synthesis requires that

both the technical modeling community and those who ben-

efit from or participate in the use of these models are

addressed.

DEFINITIONS

This section identifies and explains key travel demand fore-

casting and modeling terms. Explanations of other terms(e.g., HOT lanes) are provided only if they are pertinent to

travel demand and revenue forecasting.

Alternative definitions may exist for some of these terms;

the definitions presented here represent how the terms were

understood in the context of this synthesis. These definitions

are complemented by a glossary of terms found at the end of

the report.

• Congestion pricing (or value pricing)—Use of pricing

as a means of managing traffic, through the imposition

of a premium fee to road users who choose to drive dur-

ing peak periods, such as rush hour or holiday week-

ends. Tolls vary according to the level of congestion;

for example, higher tolls are charged during peak hours

of operation or peak direction of travel (7 ). (See also

variable pricing.)

• Critical review (or audit or second opinion)—Process

by which an independent review is conducted of the toll

demand and revenue forecasts, the model on which

these are based, and the input data and assumptions.

This is distinguished from a peer review, which is con-

ducted during the calibration and development of the

model, thus providing input to the actual development

6

of the model (and not necessarily examining any resul-

tant forecasts). (The terms are used loosely and inter-

changeably in practice).

• Feasibility study—Examination of the feasibility of

implementing a proposed toll facility or a network of

toll roads. It is generally not used as the basis for fund-

ing, but rather as the basis for determining whether thesubject warrants further investigation. The feasibility

study might test alternate facility or network configura-

tions, different assumptions of other competing routes

or modes, demographic and economic forecasts, etc.

• High-occupancy toll (HOT) lanes—Limited access

roads that provide free or reduced-cost access to quali-

fying HOVs, but also provide access to other paying

vehicles that do not meet the occupancy requirement.

By using price and occupancy restrictions, the number

of vehicles that travel in these lanes can be managed (8).

• Investment grade traffic and revenue forecast—A more

detailed estimate of the traffic demand and revenues for

a specific proposed facility. The term is used in differ-ent ways in the practical literature (which may reflect,

in part, some of the observed problems regarding relia-

bility, accuracy, and credibility of the forecasts). How-

ever, a general definition that has been accepted in the

financial community is that an investment grade traffic

and revenue forecast represents a forecast that can form

the basis for credit ratings, financing approval, and the

sale of capital markets debt.

• Ramp-up period—Time for traffic volumes to reach

their full potential, without considering growth, after

the opening of a new toll facility. The ramp-up period,

which can last for several years, is the time it takes for

users to become aware of the new toll road, change theirtravel patterns accordingly, and recognize the potential

time-savings of using the new toll road (9).

• Revealed preference survey—Quantitative survey of

observed travel behavior in the study area. These sur-

veys record how people actually travel over a certain

time period (e.g., a 24-h period on a “typical” weekday)

or at a certain point in time. The surveys capture the trip

origin and destination, its purpose (e.g., the home-to-

work commute and going shopping), the mode used

and, in some cases, the start and end time of the trip.

Related questions are also often asked (e.g., whether the

driver paid for parking or whether a transit rider could

have used a personal vehicle). The surveys can be con-

ducted with a representative sample of homes in an

urban area, generally by face-to-face interview, tele-

phone, or mail (household origin–destination survey,

capturing all the trips made by household members over

a period of time), by interviewing drivers “intercepted”

along a road or highway of interest (the interview typi-

cally covers the current trip), goods movement surveys

(of truck drivers and dispatchers, etc.), and so on.

• Risk assessment—Quantitative or qualitative estima-

tion of the incidence and magnitude of an adverse

effect on a given population (10). In the context of toll



7

road revenue forecasts, this often refers to the values of

various inputs (e.g., forecasts of population or of the

value of time); assumed configurations of the trans-

portation network, such as the timing of planned com-

peting routes or modes; or the treatment of specific

components of the forecasting process (e.g., the meth-

ods used to estimate ramp-up traffic). It also can referto inherent uncertainties in the modeling process and

structure.

• Risk management—Uses the results of a risk assess-

ment to develop options for addressing or mitigating the

identified risk, and subsequently evaluating and imple-

menting these options (10). In the context of toll road

revenue forecasts, this could refer to the development

of alternate scenarios (e.g., a range of population pro-

jections) or sensitivity tests to be run in the model to test

the variability of the toll road demand with regard to

alternate scenarios.

• Stated preference survey—Survey that attempts to

quantify how travelers would behave in a situation thatis new to them. These surveys are typically used to esti-

mate the value of time for proposed toll facilities, which

generally cannot be captured in revealed preference sur-

veys. (Thus, a revealed preference survey provides a

general quantification of the distribution, magnitude,

and characteristics of a region’s or corridor’s travel

activity; whereas a stated preference survey is used to

estimate the impact of the imposition of pricing on the

routes that the travelers who generate this activity

would take.) Stated preference surveys are designed to

present different options to respondents; for example, to

determine not only the value of time but also how their

perceptions of that value would vary by time of day

(i.e., by congestion level).

• Value of time (VoT)—Monetary value given by travel-

ers to travel time. VoT is used in the forecasting process

to relate how the value of tolls influences route choice(i.e., the driver’s decision to use the tolled facility rather

than a non-tolled alternate route). VoT varies by indi-

vidual, trip purpose, mode, average income levels, or

time of day.

• Variable pricing—User charge that varies by time

period as a way to manage travel demand and reduce

congestion. This is the basis of congestion pricing. Such

fees are higher during peak periods when the conges-

tion is most severe and lower during off-peak periods

when there is minimal congestion. This concept is sim-

ilar to many services, such as telephone service, electric

utilities, and airlines that use time-variable pricing to

encourage more efficient use of system capacity andallow users to save money by shifting their consump-

tion to off-peak periods (11).

• Willingness to pay (WTP)—Value of time that

accounts for how much travelers value different attri-

butes of the proposed facility, as opposed to simply its

availability. The average WTP can be greater than the

actual value of time, and can vary by time of day (i.e.,

as congestion increases). The WTP can reflect drivers’

expectations of what the tolled facility offers, such as

improved safety and reliability.



This chapter describes the derivation of the information upon

which this synthesis was based. The information was gath-

ered in two separate tasks: a literature review and a survey of

practitioners. The tasks were conducted in parallel, seeking

similar information.

LITERATURE REVIEW

The literature review began with a search for any resources

that had the potential for further review. An online search wasconducted using traffic resource websites and search engines

to gather available electronic resources. This was followed by

contact with representatives of different DOTs, toll authori-

ties, bond rating agencies, and bond insurance agencies, as

well as with academics, to request resources from the search

that were not available online. The agency representatives

were also asked to provide any other sources of information

(e.g., published reports, journals, and articles), as well as the

names of any other individuals or organizations that might

provide further assistance.

The literature review and sources of data focused primar-

ily on U.S. practice and experience. However, there are sev-eral international projects that have come into existence

within the last 10 years in North America and in Europe.

These are relatively state of the practice and provide the

added benefit of being completed studies with tangible

results (e.g., the Highway 407 Electronic Toll Highway in

Canada). In such cases, resources were also compiled based

on international project experience.

Relevant publications and reports were located by various

search methods including, but not limited to, the following

five sources of information:

• Online Transportation Research Information Service

(TRIS);

• The state of the practice survey;

• Contacts from the DOTs, toll authorities, bond rating

agencies, and bond insurance agencies (including mem-

bers of the topic panel);

• The consultant’s internal library; and

• The Travel Model Improvement Program (TMIP-L)

Digest.

TRIS Online is the web-based version of the TRIS database.

It provides links to full text and to resources for document

8

delivery or access to documents where such information is

available. These may include links to publishers, document

delivery services, and/or distributors. It is the largest and most

comprehensive source of information on published transporta-

tion research on the Web. TRIS Online provides access to more

than 500,000 records (at the time of this synthesis) of published

transportation research through a user-friendly searchable

database. Sources of information found through TRIS included

published articles and journals, and academic literature, as well

as conference papers and presentations.

The state of the practice survey, Estimating Toll Road

Demand and Revenue, asked respondents to forward copies

of any reports that might be of interest or relevance to the

synthesis. Several of the respondents included traffic and

revenue studies, rating agency reports, or earnings reports,

which were added to the literature database.

Contacts at the DOTs, toll authorities, bond rating agen-

cies, and bond insurance agencies provided hard copies or

electronic versions of various reports where possible (i.e.,

feasibility studies, risk assessment studies, traffic and rev-

enue studies, etc.). They also provided information as towhere online publications could be found.

The consultant’s internal library was an important source of

reports, including traffic and revenue studies, travel pricing

strategies, highway finance theory, and practice research. It was

also a secondary source for published articles on practice of toll

roads with respect to modeling, revenue, forecasting, and more

general topics such as electronic toll and traffic management.

Another secondary (although noteworthy) source used for

acquiring literature and data was through a TMIP-L Digest

discussion group, which is used by many modelers in the

United States and elsewhere to raise technical issues. Some

of the participants were contacted with respect to information

or opinions expressed regarding toll road forecasting and

modeling in this forum. They were asked to clarify or

amplify these opinions and provide information and data.

SURVEY OF PRACTITIONERS

A web-based survey was sent to four types of organizations

throughout the United States: state DOTs, toll authorities, bond

rating agencies, and bond insurance agencies. The survey

CHAPTER TWO

METHOD FOR LITERATURE REVIEW AND SURVEY



9

included both the “traditional” transportation community (the

first two types) as well as the financial community (the last two

types). This diversity in the survey group was intended to cap-

ture the viewpoints and experience of the forecasting and mod-

eling process from as many participants involved in the process

as possible.

Participants were given the option of answering directly

online through a web-based survey program (Websurveyor)

or completing a hard copy of the survey that was included in

the e-mail as a pdf, which could be returned to the consultant

by mail or by fax. A pilot test of the content, structure, and

format was conducted before the survey launch.

The survey was divided into three self-contained sections

(designated as Parts I, II, and III). This made the survey more

“respondent friendly,” to specifically target areas of interest

in the modeling and forecasting process.

Part I determined the type of agency that was respondingto the survey and who from that agency was completing the

questionnaire. It also asked them to discuss their philosophy,

in terms of their use of tolling technologies, what type of

facilities were tolled, etc.

Part II pertained to the forecasting model itself and its

variables. This section was answered either by the original

respondent, if the responding organization performed the

modeling and forecasting in-house, or could be forwarded to

a consultant or the agency that actually developed or applied

the model for the original responding agency. This section

requested that the respondent describe in detail the type of

model used and the parameters of the model in terms of

inputs, structure, modeled trip purpose, calibration tech-niques, validation checks, etc.

Part III asked the respondent to discuss a specific exam-

ple of a toll road traffic demand and revenue study carried

out by the responding organization. Again, if the original

respondent did not perform the actual analysis, the survey

could be passed to a consultant or outside agency responsi-

ble for the study. The purpose of this section was to deter-

mine the results of the previously described model (Part II)

and whether there were major or minor problems with the

analysis, whether they were identified, how and if they were

corrected, etc.

In sum, 138 surveys were sent to different organizations.

There were 55 respondents, for a response rate of 40%. Of

these, 29 declined to complete the survey or completed only

Part I of the survey, because they did not currently or plan

to own or operate toll roads. The remaining 26 respondents

completed Part II, with 13 of these completing the entire

survey.



This chapter documents the state of the practice in travel

demand forecasting for toll revenues. It begins by describing

travel demand models (the basis of the toll road traffic fore-

casts), how they have evolved generally and specifically for

toll road forecasting, and how the models relate to revenue

forecasts. The specific problem of the performance of these

models in toll road applications is illustrated by a comparison

of projected and actual revenues from several facilities and a

discussion of the factors that influence performance. Current

and emerging practices in the treatment of these factors arethen described, based on the literature search and survey.

TRAVEL DEMAND FORECASTING MODELS,APPLICATIONS, AND EVOLUTION

This section briefly reviews the practice of travel demand

forecasting models and how these models and their applica-

tions have evolved over the last several decades. The purpose

is to provide a context for the ensuing discussion, at a level

of detail and at a perspective that are appropriate to the dis-

cussion of toll road traffic forecasts. The discussion is not

intended to replicate the many existing texts on forecasting

[to which the reader can refer for further details—see, for

example, Meyer and Miller (12)].

Overview of Travel Demand Forecasting Models

The demand for travel is a derived demand. People travel (and

goods are shipped) as a function of human activities. These

activities are commonly represented in a travel demand fore-

casting model as demographic, socioeconomic, and land-use

variables (e.g., population, employment, and jobs).

Travel demand also is shaped by, and shapes, the trans-

portation network. The “supply” of transportation services—

the different modes, their relative costs (time-wise and

money-wise, temporally and financially), and the relative

ease of accessing one location versus another—determine

how the demand uses the transportation network. Similarly,

forecasts of demand define the required supply of trans-

portation services (how many lanes of road at what capacity,

where bus routes are needed, etc.).

Many medium- and most large-size urban areas in the

United States, and around the world, use a travel demand fore-

casting model, albeit with various approaches and to varying

10

degrees of detail and sophistication. In the United States,

MPOs use models to develop long-range transportation plans.

Consistent with this plan, a transportation improvement pro-

gram must identify a list of projects proposed over a 20-year

(or longer) period. The program also must identify priorities

for the next 3 years (and must account for all federally funded

projects over that time) and must be updated every 2 years. In

addition, as a basis for improving urban air quality, federal

regulations require that long-range transportation plans be

consistent with air quality objectives and targets (13).

The so-called “four-step” modeling process represents the

most commonly used formulation for travel demand fore-

casting models (12). The process has been used for several

decades in the United States and around the world. Figure 1

presents a generic outline of the main inputs, processes, and

outputs of this travel demand modeling paradigm. The indi-

vidual elements are described here (14).

Inputs

• Zone definition. The urban area is divided into smallspatial analytical areas, similar in concept to census

tracts. Generally, traffic zones are defined by homoge-

neous land uses (residential neighborhoods, central

business districts, industrial areas, etc.), major “traffic

generators” (universities, hospitals, shopping centers,

airports, etc.), or geographic boundaries (rivers, rail-

ways, etc.).

• Land-use inputs. These are defined for each traffic zone

in terms of population, employment, floor space, etc.

• Transportation network. This normally includes the

major road and highway network (typically, all roads

except local streets), as well as the public transport net-

work (bus routes, subways, light rail, commuter rail,

etc.). These are defined in terms of a link-node network.

The network can be refined to differentiate between

HOV lanes, bus lanes, truck routes, routes with

restricted access, etc. The network also can account for

tolls. Traffic zones are represented as “centroids” (a sin-

gle point each on the map), and are linked to the main

network by means of “centroid connectors.”

• Observed travel characteristics. This is measured typi-

cally by origin–destination (travel characteristics) sur-

veys. These provide a quantitative portrait of travel

characteristics in a city, typically on a weekday.

CHAPTER THREE

TOLL ROAD FORECASTING: STATE OF THE PRACTICE



11

Traditional origin–destination surveys are “revealed

preference” surveys—that is, they observe how people

actually behave. However, these have proved limited as

predictors of conditions that do not exist currently in aspecific city: particularly the use of a new transit tech-

nology (notably, rail) where none currently exists, and

the willingness to use a tolled highway where tolls are

currently not in place. “Stated preference” surveys

attempt to quantify and predict such behavior. Counts of

vehicles and their occupants by type of vehicle, at various

points through the road and transit networks, are also

important inputs. Other inputs include link/intersection

(node) travel times and speeds. All of these observed

conditions are used to calibrate the model.

Some urban areas are beginning to conduct “activity”-

based surveys, which provide a more precise depiction of

travel characteristics within the context of a household’s

daily activities. In comparison, the origin–destination survey

focuses on these travel characteristics alone.

Process

The four steps of the process comprise:

• Trip generation—where the total numbers of trips that

start and end in each zone are calculated as a function

of the different land uses in each zone. The calculations

take into account different trip purposes, which again

are represented by land uses (e.g., the daily home-to-

work commute is commonly represented by populationor dwelling units at the home end and by the number of

jobs at the work end).

• Trip distribution—where the generated trip ends are

distributed among all zones. The distribution is con-

ducted as a function of the zonal land uses (e.g., home-

to-work trips would not be distributed to zones where

there is no employment) and the characteristics of the

transportation network (i.e., a function of the relative

accessibility of a zone, which is measured as a function

of travel time–congestion and cost–transit fares, park-

ing charges, road tolls, etc.). Different calculations are

made for different trip purposes to take into account

their different behaviors. The products of this step are

expressed as matrices of trips for different purposes

(e.g., stating that there are 100 trips for purpose “x”

from zone i to zone j).

• Modal split—where the distributed trips are allocated

to the different available travel modes. Typically, the

allocation is between automobiles and public trans-

port; however, some models further differentiate

among public transport modes (including park and

ride), between HOV (i.e., automobiles in which there

are two or more occupants) and SOV (i.e., automo-

biles in which the only occupant is the driver), and

Inputs: ZoneDefinition

Inputs: Land Use /

SocioeconomicVariables

Inputs: Road and

Transit Networks

Inputs: Origin -Destination TravelSurvey, Counts, etc.

Trip Generation

Trip Distribution

Modal Split

Trip Assignment

Outputs:

vehicles / hour and transitpassengers / hour onnetworks; link speedsand travel times

Simulated

FIGURE 1 Outline of travel demand modeling process (traditional “Four Step” paradigm).



nonmotorized modes (pedestrians and bicyclists). Dif-

ferent calculations may be made for different trip pur-

poses, again to account for their different behavior;

however, these are subsequently combined by mode

for the next step.

A common formulation is the logit function, which

simulates the traveler’s utility according to out-of-pocket cost, door-to-door travel time, and other attri-

butes of modal choice (such as trip distance, proximity

of the transit stop to the workplace, in-vehicle comfort,

the number of transfers required, and so on). Other,

simpler formulations include diversion curves or fac-

tors. Different formulations may be applied to different

trip purposes. Typically, the resultant matrices for a

given mode are combined for all purposes, resulting in

a matrix of all automobile driver trips, all transit pas-

senger trips, etc. The survey of practitioners indicated

that the inclusion of modes varied. All respondents (i.e.,

those that had completed all three parts of the survey)

indicated that passenger vehicles were modeled; how-ever, not all differentiated between SOV and HOV

modes of travel. Most of these respondents included

trucks and commercial vehicles as mode choices, and

about half of these included transit in the model. The

methods for mode choice modeling included logit (or

similar) and other factors. Some of the respondents

using assignment-only models calculated modal choice

exogenously.

• Trip assignment—where the trips for each mode are

loaded onto, or assigned to, the respective transporta-

tion network(s). This is a translation of demand, which

is expressed as the number of trips by mode x (for all

purposes combined) between zone i and zone j, intoautomobile traffic volumes on a given road link and rid-

ership on a bus route, etc.

Of interest to this synthesis is the treatment of auto-

mobile driver trips [which are equivalent to automo-

bile vehicle trips (i.e., there is only one driver per

vehicle)]. There are several algorithms for assigning

automobile vehicle trips. The “equilibrium assign-

ment” is a common technique. This process allocates

traffic to links so as to minimize the cost of the auto-

mobile or transit traveler between his or her origin

and destination; where “cost” is commonly defined as

travel time (minutes) and, in some models, with a

monetary cost expressed in terms of time (i.e., value

of time). The latter allows for the impact of tolls or

other pricing mechanisms on the driver’s choice of

route. Equilibrium is achieved when, between the

current and previous iterations, no driver (i.e.,

vehicle-trip) can improve his or her travel time by

switching routes. In contrast, the so-called “all-or-

nothing” assignment algorithm does not account for

the build-up of volume or cost, and assumes that all

link speeds (typically, the posted or free-flow speed)

remain fixed without regard to the actual volume on

each link.

12

Outputs

Volumes by link and ridership numbers are the main outputs

of the model, along with travel times and speeds across the

transportation network by link. These outputs can be used in

turn to identify costs, fuel consumption, and air pollutants, as

well as revenues on a tolled facility.

Comments on Modeling Process

It is important to note that the aforementioned modeling

process is not prescribed; that is, there is no one single or

standard modeling process or universal method. Keeping in

mind the different perspectives and uses of toll road demand

forecasting, some comments are in order.

• Commonly, MPOs and other transportation planning

authorities focus on simulating peak-hour travel on the

transportation network—that is, the time of day at

which the transportation system carries its maximumvolume. Normally, this occurs during the morning or

afternoon commuter peak periods. However, the model

may simulate different time periods, ranging from 24-h

travel on a typical weekday to a peak period or a peak

hour within that period. Factors may be used to derive

peak-hour matrices from 24-h or peak-period matrices;

however, in the absence of these factors or of direct

modeling of the peak hour or period, the use of sophis-

ticated algorithms (such as the equilibrium assignment

technique) is effectively precluded.

In contrast, toll road revenue forecasts typically

require annual estimates of demand. Therefore, fore-

casts from the aforementioned peak-hour models mustbe extrapolated. This requires the development of fac-

tors for different time periods, which can include the

peak period, daily, weekly, monthly/seasonally, and

ultimately, annually. Factors may be developed

according to observations of traffic volumes or trends

(e.g., 24-h traffic counts, by hour) or other sources.

However, the use of factors maintains the status quo,

does not account for temporal changes (peak spread-

ing) or mixes in the traffic composition, and may

require special additional factors or assumptions to

account for travel on weekends or holidays. Some

models have addressed this by simulating several time

“slices” during the day (e.g., the a.m. peak hour, a mid-

day hour, and the p.m. peak hour).

• Some models emphasize certain steps more than oth-

ers, or the models may not include some steps or have

combined others, or some parts of the process may be

modeled exogenously. For example, the Quèbec

(Canada) Ministry of Transportation’s urban models

for Montrèal and other cities focus on the trip assign-

ment step, using trip matrices that are derived directly

from comprehensive high-sample, origin–destination

surveys. Modal shares and demand forecasts are devel-

oped exogenously to the assignment model, although



13

trip generation and trip distribution typically are not

modeled. The point is that the treatment of a (nomi-

nally) common modeling process varies among trans-

portation planning authorities, which differences in

turn necessarily are carried through in the treatment of

toll demand forecasting. As a result, the comparability

of toll demand forecasts and their performance may belimited.

• Truck and commercial traffic is generally considered to

be an important market segment for many toll roads.

However, relatively few urban models simulate these

trips explicitly. Some urban areas have developed truck

models, which may or may not be integrated within the

primary urban passenger travel model. A common

treatment for including truck or commercial traffic is to

factor the resultant automobile forecasts on each link

according to the observed proportion of trucks or com-

mercial vehicles in the observed traffic mix (according

to traffic counts). Although this provides a simple tech-

nique for capturing the “full” mix of traffic on a partic-ular facility, on its own it provides no way to account

for tolling, other changes to the transportation system,

or changes in demand. Moreover, the truck peak hours

in many urban areas do not coincide with that of the

dominant automobile peak hour.

Evolution of Models

The modeling process has evolved since the development of

the four-step modeling process during the 1950s and 1960s.

At that time, models were applied primarily to the planning

of major transportation facilities (mainly highways) toaccommodate rapid post-war urban growth. The four-step

process is still the dominant formulation in urban travel

demand models (12, p. 289). However, several concerns

have encouraged the development of new techniques.

• The ability of the process to address current planning

needs, which have evolved from the planning of new

highways to meet forecasted demand to better manag-

ing that demand (e.g., through other modes as well as

traffic management).

• Inconsistencies among the four steps have been identi-

fied with respect to their formulation, parameter values,

costs, and variables (15). These inconsistencies have led

to questions regarding their depiction of traveler behav-

ior. In addition, the four-step process treats travel

choices as independent choices, whereas in reality they

are not mutually exclusive. For example, the decision to

make a trip in the first place (generation) may be a func-

tion in part of the availability of a particular mode

(modal split). Some models have addressed this by com-

bining steps [e.g., trip distribution and modal split (15)

or the trip generation, distribution, and modal split (14)].

Other models have attempted to address this more

simply by introducing feedback loops among the steps

[i.e., which is not inherent to the four-step process

(16 )]—notably, the travel times that result from the trip

assignment are fed back to trip distribution to provide a

more realistic depiction of the “true” travel times

between zones, with the distribution–modal split-

assignment process then iterating several times until an

equilibrium is reached.• Similarly, there is inadequate feedback between the

travel demand forecasting model and its land use

inputs. The implication is that the changed travel pat-

terns can affect the distribution, magnitude, and type of

development over time (e.g., an expressway extension

that improves accessibility to a new suburb), which in

turn affects the characteristics of travel demand. Efforts

in different U.S. cities and elsewhere to develop inte-

grated land use and transportation models have been

documented (17 ). In addition, several techniques have

been used to forecast these land use inputs (18): the

importance for toll road demand forecasting is that

there is no consistency in modeling technique; this timefor key inputs.

• Forecasting methods can be divided into two broad

groups: macro-analytical methods, which are based on

zonal averages, and micro-analytical methods, which

are based on individuals and households. The four-step

process is in the first group. Because of their low cost

and technical simplicity, macro-level forecasts remain

popular; however, it is precisely these two reasons that

lead to questionable and inaccurate results (18). In con-

trast, micro-level forecasts can predict impacts with

more detail and accuracy.

More generally, the development of micro-level

forecasting capabilities also addresses the behavioralinconsistencies identified previously (simultaneous

choices, lack of feedback, etc.), through the use of

activity-based models. This approach treats travel as

being derived from the demand for personal activities,

so that travel decisions become part of an individual’s

broader activity-scheduling process. In turn, activities

are modeled, rather than only trips. The basic travel

unit is a tour, which is defined as “the sequence of trip

segments that start at home and end at home” (19). This

allows for a more consistent and inclusive treatment of

the individual’s decisions (when, where, why, and how

to travel); links these decisions for all of an individ-

ual’s trips over the course of the day, allows the deci-

sions to be analyzed in the context of the decisions of

other members of the households, and, allows for con-

sideration of lifestyles (e.g., “commuting” by Internet)

(12). The resultant chain of decisions means that

higher-level decisions are fully informed about lower-

level decisions (i.e., decisions are “nested”) (20).

Emerging methods also allow the simulation of an

individual’s activities dynamically, meaning that this

micro-level treatment eliminates the need for zonal

aggregations, allows the heterogeneous (travel) char-

acteristics of the population to be analyzed, and has the



potential to generate “emergent behavior” (i.e., behav-

ior is not explicitly “hard-wired” into the model, based

on its calibration to conditions at a particular point in

time) (12).

• Methods to model time-of-day choice are emerging.

This refers to the relative lack of consideration of tem-

poral considerations in demand modeling—that is, thetraveler’s choices are related to choices regarding the

time of day in which the trip is made. Time-of-day

choice can be expressed in terms of the time “slices”

that are modeled; the days that are modeled (e.g.,

weekday versus weekend or holiday); peak spreading

(i.e., the allocation of trips between the peak hour or

half-hour and the peak “shoulders,” as the expansion

of the duration of the peak period over time); and

time-of-day choice modeling [i.e., the explicit model-

ing of the time at which the traveler starts his or her

trip in order to arrive at a destination within a desired

“envelope” (e.g., between 8:45 and 9:00 a.m. every

morning)].The aforementioned need to develop improved fac-

tors for expanding peak-hour volumes to yield annual

revenues is one manifestation of the importance of

time-of-day modeling to toll road demand estimation.

Also important is its potential to depict more accurately

a traveler’s response to congestion: rather than switch

routes (to an uncongested toll road), the driver may

advance or delay the start time of his or her trip to a less

congested time of day (or simply not travel).

Where time-of-day choice is considered in practice,

the most common consideration has been peak

spreading. One peak spreading model accounted for

congestion when determining the proportion of a.m.peak-period vehicle traffic. It was hypothesized that

“the total congestion for a trip is a primary reason for

peak spreading rather than the congestion of, possi-

bly, one link,” thus establishing the need to account

for congestion throughout the network in addition to

trip purpose and trip distance. In other words, the

advantages offered by a toll facility must be consid-

ered in the context of its impacts on overall network

congestion. Although the model predicted the flatten-

ing of the a.m. peak period as congestion and trip

length increase, it assumed that a constant duration of

the peak period (in this case, 3 h). That is, the pro-

portion of daily travel that occurs in the 3-h peak

period was assumed to remain stable over time

(which may not be appropriate in all cities or for toll

roads, especially as the duration of peak period grows

or off-peak traffic volumes increase). The report con-

cluded that both trip purpose and trip distance, in

addition to congestion, were important parameters in

a model that predicts peak spreading (21).

A more recent analysis concluded that the use of

dynamic traffic assignment (such as equilibrium assign-

ment) models as a means to predict the impact of new

infrastructure should account for departure time choice

14

in addition to route choice. The resultant model took

into account the need for travelers to arrive at their des-

tination, for particular trip purposes (e.g., going to

work, to classes, or to an appointment), at a particular

time, which in turn determines their departure time.

Each traveler has a preferred departure and arrival time,

any deviation from which (owing to congestion) causesdisutility. The dynamic traffic assignment models time

choice simultaneously with route choice (22). Another

emerging development is the use of “equilibrium

scheduling theory,” which simulates departure time

choice modeling in the context of an equilibrium net-

work model. This approach models the build-up and

decay of travel times during the peak periods, taking

into account the disutility of arriving before or after a

preferred arrival time window (23).

• Network micro-simulation models have come into use

as tools to simulate the dynamics of traffic along cor-

ridors and networks. Whereas the travel demand fore-

casting models simulate average speeds for an hour’sslice of traffic, these models use micro-simulation tech-

niques to represent traffic flows microscopically through

a network as a series of individual vehicles and tracks

each vehicle’s progress at a finite resolution, which is

typically one second or less. This logic permits consid-

erable flexibility in representing spatial variations in

traffic conditions over time and allows for the analysis

of such traffic phenomena as shockwaves, gap accep-

tance, and weaving. The importance of network micro-

simulation models to toll road demand forecasting lies

in the emergence of managed lanes as tolled options in

several cities; specifically, in their ability to simulate the

dynamics of individual lanes and the diversion of driversbetween lanes.

Network micro-simulation models provide a more

detailed approach by taking into account the transient

effects on speed and acceleration as the vehicle travels

on a road network. By modeling vehicle kinematics

(instantaneous speed and acceleration) on a road net-

work, more reliable estimates of vehicle energy con-

sumption and emissions result.

Typically, micro-simulation traffic models are appli-

cable only to sub-networks of larger urban areas. This is

a result, in part, of the required level of detail necessary

of model inputs and the subsequent strain these require-

ments place on computing capabilities. The dynamics of

individual vehicles are defined in terms of the number of

departures from each origin–destination pair, the deter-

mination of vehicle speed based on car-following logic,

and requirements for lane changing. The speed of the

vehicle along that first link, as well as any subsequent

links, is updated at discrete time intervals typically

between 0.1 and 1.0 s. Each update reflects the distance

headway between the vehicle in question and the vehi-

cle immediately preceding this vehicle; whereas the

exact speed for any given distance headway is based on

a link-specific, car-following relationship. Beyond the



15

speed restrictions, which arise from the above car-

following logic, a vehicle’s progress can also be delayed

at traffic signals, ramp meters, queues, and/or other

bottlenecks. The effects can be time-varying and may

vary both spatially and temporally, which permits the

replication of shock waves within the model. When a

vehicle travels down a particular link, it may make dis-cretionary lane changes to maximize travel freedom

(speed). Conversely, mandatory lane changes may be

required owing to the prevailing network geometry and

routing behavior (24).

In sum, these developments provide opportunities to

improve the overall state of the practice in travel demand

forecasting, as well as that of toll road demand forecasting.

However, many of these developments are emerging: there

is only a very small number of practical applications of

activity-based models in the United States (20) and few ap-

plications of time-of-day choice modeling. The TRANSIMS

initiative of the federally funded TMIP also can be expectedto affect transportation planning practice (13). Conversely,

network micro-simulation models are well-established in

transportation planning practice, with several recent man-

aged lane applications.

Methods for Modeling Toll Road Demand

No state-of-the-art consensus exists among transportation

researchers and practitioners regarding the best methods

for achieving traffic and revenue forecasts (25). This mir-

rors the general application of models in transportation

planning practice. Methods being used today can still becategorized primarily by incremental or synthetic analysis,

both of which the transportation planning community has

been using. The choice of analytical method varies, based

on the method that is used to develop origin–destination

trip tables for a given time period, trip purpose, and travel

market segment (25).

A review of the state of the practice for value pricing

projects in several U.S. cities identified the following five

categories of modeling procedures (20). Although the review

primarily addressed forecasts for managed lanes, the catego-

rization is applicable more generally to toll road demand

forecasts.

1. Modeled as part of an activity-based model—The state

of the art in demand modeling allows for the inclusion

of pricing into the decision hierarchy. A combination

of revealed and stated preference surveys could be

used as the basis, with the stated preference data allow-

ing for the modeling of choices that do not yet exist.

Only Portland, Oregon, a pioneer in the development

of activity-based models, has applied this type of

model to the subject (i.e., to an analysis of value pric-

ing). The practical use of activity-based models in

transportation planning is only now emerging and rep-

resents a significant effort (20). Only one respondent

to the survey of practitioners indicated the use of an

activity-based model, which was applied to a toll

bridge.

2. Modeled within the modal split component of a four-

step model—Automobile trips on a tolled or non-tolled road are considered as distinct modal choices,

with separate modal split functions for work (or work-

related) and non-work trip purposes (given the corre-

sponding differences in values of time). The advantage

of this approach is that out-of-pocket costs can be

modeled explicitly, because travelers’ utilities are

“directly affected by the value of tolls and so are the

respective modal shares”—that is, the approach

ensures “robustness” in the results. The approach also

can be expanded to trip distribution modeling, because

the impedance incorporates the impact of tolls more

explicitly. The ability to incorporate stated preference

data into revealed preference data, as a means toaccount for nonexistent facilities, again was noted

(20). Phoenix, Arizona, and Sacramento, California,

were cited as examples of urban areas that have used

this approach. The Phoenix [Maricopa Association of

Governments (MAG)] model distinguishes between

the SOV trip and the HOV trip. Because MAG allows

vehicles with only two occupants to use its HOV lanes,

the tolled/non-tolled choice is included only in the util-

ity function of the SOV trips. The function includes a

travel time savings term that is equivalent to the dif-

ference between tolled and non-tolled travel time.

MAG’s trip distribution model was being updated to

account for these impedances (20).The analysis for the Minneapolis–St. Paul man-

aged lane system (MnPASS) incorporated tolled

SOV trips as a modal choice into the regional model.

Values of time were developed for two trip purposes

(home-based work and other trips), both classified by

three automobile availability categories (number of

vehicles per household, which had been found to be

a determinant of value of time) and whether the trip

was destined to the central business district (again,

found to be a determinant). The basic values were

adapted to this model from previous local studies or

from experience elsewhere, given that the time frame

available for the analysis precluded the collection of

new data. The revised modal split function was used

to screen alternative network configurations. For the

purpose of the analysis, the resultant revised imped-

ances were not implemented into the trip distribution

component, to allow the alternatives to be compared

on a common basis. In other words, although behav-

iorally the tolls (i.e., the revised impedances) would

affect trip distribution (given the appropriate feed-

back loops), it was felt that the impacts would be

small when compared with the ability to compare

alternatives (26 ).



The MnPASS study used value-of-time data from an

evaluation of the impacts of the Riverside Freeway

(SR-91) tolled express lanes in Orange County,

California. Based on observations from 3 years of

operation (after the express lanes opened in 1995) and

from traveler surveys, the evaluation found that gen-

der (female) was a strong determinant of the use of thefacility, with other factors [high income, middle age,

higher education, and commuting to work (i.e., work

or work-related)] also being indirect factors (indirect

in that they determined the willingness to purchase an

electronic transponder, without which drivers were

unlikely to use the express lanes). Logit choice mod-

els were developed according to these factors (27 ).

The SR-91 evaluation identified several important

determinants of managed lanes—gender, income, age,

education, and trip purpose. Although the mix of deter-

minants and their values might vary by location, there

is (for example) an observed correlation between trav-

elers’ income and the likelihood of using toll roads,with higher-income travelers more likely users than

lower-income travelers (20).

The importance of considering time-of-day impacts

was underscored by a recent study of the impacts of the

Port Authority of New York and New Jersey’s time-

of-day pricing scheme, which it introduced in March

2001. The study found that 7% of passenger trips and

20% of truck trips changed behavior because of the

new pricing scheme. The percent share of peak shoul-

der trips for both trucks and automobiles also

increased during weekdays (28).

The modeling of toll demand as part of modal split

requires that the generalized cost impedances (i.e.,impedances that account for monetary values—such

as tolls—as well as travel times) are fed back from trip

assignment to trip distribution and modal split. The

process iterates until a stable equilibrium is achieved;

that is, when there are no significant differences in the

impedances between two iterations (29).

3. Modeled within the trip assignment component of a

model—This approach applies a diversion of trips

within the trip assignment; that is, after (or in the

absence of) demand modeling. It assumes that trip dis-

tribution and modal shares (not differentiating

between tolled and non-tolled automobile trips)

remain unchanged in the absence of feedback loops.

There are two general methods for modeling traf-

fic diversion in trip assignment: The first translates

the monetary toll into a time equivalent through the

use of values of time. The equivalent times are then

incorporated into the model’s volume-delay func-

tions, which—using the equilibrium assignment

technique—are used in turn to allocate trips among

different paths according to travel time, capacity,

and congestion. Queuing and service time at toll

plazas similarly can be incorporated into the func-

tion. (In essence, the tolls and plaza times are added

16

as “penalties” to the modeling of actual travel time.)

Values of time can be derived for different trip pur-

poses, income levels, etc. (25).

The second method uses diversion curves as the

basis for toll forecasts. This commonly takes the form

of a logit function, which calculates the propensity to

use a tolled facility (the facility’s share of traffic) as afunction of the relative cost or travel time between

the tolled and non-tolled route (i.e., for each origin–

destination path that could use the tolled facility). The

slope on the S-shaped diversion curves represents the

elasticity of demand with respect to the relative cost

or travel time using the tolled road. The elasticity of

demand is related inversely to the value of time or

willingness to pay. The shape of the curve can be

determined in two ways: using observed data (in

which case the value of time is implicit) or from a sta-

tistically estimated logit function based on revealed

and/or stated preference survey data. The curves can

be fitted according to different trip purposes and vehi-cle occupancies. The diversion curve is applied to the

relevant trip table (for a given purpose, income group,

automobile occupancy, time period, etc.) to derive

tolled and non-tolled trip tables. These then are

assigned to the network to yield both updated imped-

ances (and the process is repeated until an equilibrium

is reached) and, ultimately, estimates of revenues

(25). The primary benefit of using diversion models to

estimate toll road demand is that they can be applied

to an existing four-step model, without having to

recalibrate it. However, the shape of the curve, and the

data upon which it is based, generally are held as con-

fidential or proprietary and so are not available toother users (29).

A variation is the use of a dual minimum path (equi-

librium) assignment, which develops two sets of paths

for each origin–destination pair: one using the tolled

facility (where applicable) and one without the tolled

facility. A proportion of the total trips between each

zonal pair is assigned to each network path, according

to the relative respective total costs, which can include

vehicle operating costs as well as travel time costs and

the costs of tolls (2).

An example of the second method (diversion) is

provided by a traffic and revenue forecasting study

for a proposed toll highway near Austin, Texas. A

logit model was developed for several trip purposes,

based on a stated preference survey. The utility func-

tions for the work-related trip purposes were found

to be sensitive to traveler income. The tolling diver-

sion logit model was incorporated into the trip

assignment component of an updated regional travel

demand model. The model took into account differ-

ent payment options (cash, cash plus electronic, and

electronic only). The development of the logit model

also accounted for toll road bias (the negative

propensity to use a tolled road) and an electronic toll



17

collection bias (the increased likelihood of using a

tolled facility, owing to the convenience associated

with electronic toll collection). Both terms largely

offset each other, with the toll road bias found to be

common in regions that had no prior experience with

tolling (30). It should be noted that the assignment

impedances were not fed back to the trip distributionand modal split models; that is, the trip origins and

destinations were assumed not to change under traf-

fic diversion. The survey of practitioners indicated

that both methods were used.

4. Modeled as a post-processor—This approach can be

used either within the framework of a four-step model

or exogenously using the output of the four-step

model. Washington, D.C., and San Diego, California,

provided examples of the former, in which assigned

volumes are diverted (i.e., after trip assignment) from

general purpose lanes to managed lanes according to

the excess capacity available in the latter. An example

of the latter is provided in Minneapolis–St. Paul, inwhich the outputs of the regional model were input to

the FHWA’s Surface Transportation Efficiency

Analysis Model to calculate costs and tolls as part of a

pricing study. The procedures are operationally simple

to implement; however, they are not sensitive to

changes in traveler behavior (20).

5. Model as a sketch planning method—These are quick

response tools that are used for project evaluation.

Examples include the FHWA’s Spreadsheet Model for

Induced Travel Estimation, which estimates induced

traffic (as a result of faster facility travel speeds, traf-

fic diverted from other facilities, destinations, or

modes) as a function of elasticities of demand withrespect to travel time, with price and demand equili-

brated as part of the procedure. A modified version is

the Spreadsheet Model for Induced Travel Estimation-

Managed Lane, which uses a pivot-point logit model

to estimate changes in travel demand according to

changes in travel time and tolls as well as improved

transit service. The “model is relatively simple to

implement and can be considered a reasonable tool for

the initial screening of alternatives or in situations

where results of formal travel models are not avail-

able.” The FHWA’s Sketch Planning for Road Use

Charge Evaluation model also uses a pivot-point mode

choice model to estimate changes in mode (i.e., man-

aged lanes) and the associated revenues, costs, and

travel time delays (20).

The Texas Transportation Institute has developed a

spreadsheet-based Toll Viability Screening Tool. The

spreadsheet provides a way to assess the economic via-

bility of a proposed tolled facility in advance of the

need for a more detailed traffic and revenue forecast.

It does so by assessing the potential variability of the

initial demand, by subjecting various input parameters

to a triangular distribution function (similar to a

normal distribution)—that is, to a distribution that

measures the likelihood of their occurrence. As input,

the tool requires daily traffic volumes, toll rates, and

assumed diversion. Results (revenues) are expressed

as net present values. The tool also supports a risk

analysis of the results, taking into account the distri-

bution, which allows sensitivity tests of the inputs,

which are the most important (31).Some survey respondents reported using a spread-

sheet model to estimate travel demand for short-term

(i.e., annual) revenue forecasts of an established toll

facility, such as a bridge or tunnel for which travel

demand was stable and dependable historical data

were available. A review of literature indicated that

this was a common practice. For example, Florida

DOT’s Annual Report on its Enterprise Toll Opera-

tions stated that for older, established toll facilities its

forecasts were developed based on actual traffic and

revenue performance, with adjustments for popula-

tion growth and anticipated future events (such as new

infrastructure) (32).

The survey of practitioners did not demonstrate any con-

sistent or dominant treatments of the types of choices that

were modeled or how they were modeled. This appears con-

sistent with the practice of travel demand modeling in general.

This is supported by research that summarized the treatment

of pricing in seven models at major cities across the United

States, which found that no two of the seven models treated

the subject in exactly the same manner (33).

It is important to note that there is no fixed or standard

process for determining which type of model must be used.

Rather, the object is to ensure that the method meets the need.This was corroborated by the survey, which indicated that a

range of model types was used. For example, one respondent

to the survey, a state DOT, noted that it used two different

models for two concurrent studies. One study was determin-

ing the impacts of an impending change in the toll rate on an

existing bridge. The second study was for a proposed HOT

lane. The first study used a semi-modeling approach with

implied elasticity, using different values of time for the toll

bridge users. The respondent found this method to be effec-

tive, given the known travel characteristics of the existing

toll bridge users. The second method was fully model-based,

using a combination of a traditional four-step travel model

and a network micro-simulation model. This combination

was chosen to capture the significant impact of small changes

in traffic volumes on the HOT lane compared with the regu-

lar lanes.

Finally, it should be noted that practitioners appear to

have responded to the specific needs of forecasting for toll

roads. For example, the survey of practitioners indicated

that various combinations of time periods were modeled;

most of the cited travel demand models covered the week-

day off-peak periods in addition to the (more typical) peak

periods. In certain cases, nighttime and weekend periods



were also modeled to reflect the type of facility (i.e., the

weekend model for areas of high recreational use). A small

number of respondents incorporated time-of-day choice

into their models or used peak spreading models. More

commonly, practitioners relied on factors from other

sources (e.g., from traffic counts or from the local MPO) to

address time choice, whereas others did not include timechoice at all.

Evolution of Decision-Making Environment

Concerns regarding the reliability, accuracy, and credibility

of travel demand forecasts are not new. A 1989 U.S.DOT

study compared projected and actual ridership and costs for

10 heavy- and light-rail transit projects in 9 U.S. cities. The

study found that the actual ridership for each of the 10 proj-

ects was significantly below the projections, whereas the

actual costs were higher than the projected costs in 9 of the

10 projects. The projected ridership (i.e., benefits) and costswere used as the basis of investment decisions and of appli-

cations for federal government funding (34).

Although the study addressed only forecasts for transit, it

is relevant to toll road demand and revenue forecasts because

it received widespread attention in the transportation com-

munity and also because it anticipated many of the issues that

have since been identified in toll road forecasts. Hence, it

provides an important context for the discussion of how the

decision-making environment has evolved. For example, the

study explained the need for the community to understand

the accuracy of the ridership and cost forecasts in three ways:

the transit projects represented the largest investment ever inpublic works in each of the nine cities, local officials in other

cities where rail projects were contemplated would also rely

on similar projections to make their own decisions, and local

officials typically used similar analytical processes for other

public investments. To this end, the study found that these

“mistakes” in ridership estimates could not be explained by

differences between projected and actual values of the deter-

minants of ridership: land use inputs (which differed little

from the actual), network configurations, assumed feeder bus

configurations, or downtown parking prices (which tended to

be lower than those modeled). Each of the 10 projects was

selected among several alternatives. The study noted that

although the accuracy of the forecasts for the rejected alter-

natives could not be evaluated, for almost all of the projects

the divergence between the projected and actual ridership

and costs of the selected alternative was greater than the

entire range of the ridership and costs of all the alternatives

that were compared (which made it “extremely unlikely that

a rail project would have prevailed in the presence of more

reliable forecasts”). Rather, the study attributed the differ-

ences to the structure and nature of federal transit grant and

fund programs (effectively favoring high-capital transit

investments), which provided little incentive to local deci-

sion makers “to seek accurate information in evaluating

18

alternatives.” The result was a “bias” or “optimism” for rail

transit (35).

These differences (and those in other areas of public pol-

icy) demonstrate a “serious ethical problem” in the use of fore-

casts, with occurrences noted in which modelers had been

directed by their superiors (including local elected officials) to“revise” their ridership forecasts upwards, to “gain federal

[financial] support for the projects whether or not they could

be fully justified on technical grounds. Forecasts are presented

to the public as instruments for deciding whether or not a proj-

ect is to be undertaken; but they are actually instruments for

getting public funds committed to a favored project.” The goal

of “exaggerated forecast[s] of demand and the cost underesti-

mates” may be to “[get] the project built rather than honestly

evaluating its social benefits” (34).

Forecasts have not become more accurate over time. In a

multinational statistical analysis of 183 road projects (tolled

and non-tolled) completed between 1969 and 1998, the“forecasts [appeared] to become more inaccurate toward the

end of the 30-year period studied” (36 ). More recent fore-

casts were found to be more comprehensive than older stud-

ies; however, “this greater depth has not yet appeared to

improve the accuracy of the forecasts.” Newer forecasts did

appear to respond to earlier concern; for example, by incor-

porating better methods to forecast ramp-up volumes. How-

ever, “whether this increased scrutiny has actually led to

more accurate forecasts remains to be seen” (3).

The role of private provision of public services (such as

privately owned tolled roads) continues to evolve. Although

not specifically directed at the reliability of traffic and rev-enue forecasts, an article about “intellectual dishonesty” in

the ongoing debate may provide some context. For example,

the toll revenues for a (hypothetical) bridge that is operated

by a private company must cover its capital, operating and

maintenance costs, as well as depreciation, which reflects the

eventual need for rehabilitation or reconstruction as a result

of wear and tear. Its toll rates must be set sufficiently high to

cover these costs. Because the company accounted for

annual depreciation costs when it issued its debt to construct

the bridge (which presumably has been paid off by the time

major reconstruction is required), a one-time debt allows the

construction of a bridge that “can presumably last forever.”

In contrast, public authorities in the United States are not

required to account for depreciation, which means—for the

same tolled bridge—its toll rates could be much lower. How-

ever, it must issue new debt when the bridge is reconstructed

(i.e., the public authority inevitably must account for depre-

ciation, but does so in terms of a “perpetual debt”). This

means that the public authority’s bridge seems “less expen-

sive,” because its lower toll rates ultimately have transferred

the debt from its actual users to future generations (37 ). The

relevance to this synthesis is that the public’s expectations

and inappropriate understanding of the real costs of public

services may affect the choice of toll rates and, in turn,



19

traveler behavior (which may not be captured properly by the

forecasting models or the data upon which they are based).

A 1989 court case in the San Francisco Bay area claimed

that state and regional planning authorities had not suffi-

ciently met their obligations to reduce air pollution in their

transportation plans. Much of the resultant findings focusedon the adequacy and use of the regional travel demand fore-

casting model in predicting air quality impacts. In particular,

it took a much more “literal” interpretation of model fore-

casts than had planners historically (i.e., given the planners’

understanding of the models’ limitations owing to errors in

calibration, data input, or validation). A subsequent TRB

study found that the “analytical methods in use are inade-

quate for addressing regulatory requirements” (such as air

quality conformity analysis) (13). The relevance to this syn-

thesis is that the concerns about model inaccuracies and per-

formance that this court case identified, which preceded the

TMIP and which, in part, the TMIP was intended to address,

mirrors and anticipates similar concerns regarding the per-formance of toll demand and revenue forecasts.

RELATIONSHIP BETWEEN DEMANDAND REVENUE FORECASTS

Revenue forecasts are dependent on travel demand forecasts

and the assumptions on which the travel forecasts were

based. Critical assumptions include local growth policies, the

magnitude and distribution of future land uses, the intensity

of development, projected economic growth, changes in traf-

fic patterns, drivers’ willingness to pay tolls, and new com-

peting roads in the transportation network. The level of

uncertainty in revenue forecasts is proportional to the levelof uncertainty in travel demand forecasts.

Revenue forecasts are also dependent on the tolling tech-

nology, toll rate structure and schedule, and the stratification

of the toll road users (i.e., according to payment classes).

Tolling schemes could include discounts for electronic tolling

or multipass users, higher tolls for heavy vehicles, or variable

tolls based on time of day or section of toll road used.

Increases in toll rates can also affect the demand, especially

as some authorities have elected to increase toll rates more

sharply than projected to quickly generate revenues in the

short term (when the projected demand had not materialized).

As noted, the travel demand forecasts are commonly

developed for a weekday peak hour or peak period for sev-

eral modeled horizon years. Conversion factors are then

applied to generate daily and yearly traffic volumes. Revenue

is estimated by multiplying the forecast volumes by the toll

amount, taking into account different toll rates for vehicle

type, potential toll evasion, discounts, and other facility-

specific factors. With each assumption, a degree of error is

introduced into the revenue forecast. Another layer of com-

plexity is added when a schedule of predetermined toll rate

increases is applied to the traffic forecasts.

In travel demand forecasting, the future year forecasts

(20- to 30-year horizon) are more important and critical for

long-term planning decisions. However, for revenue fore-

casts, the initial years of operation are crucial in terms of

assessing and managing financial risk. This is because the

risk for default is typically at its highest during this period,

which is also referred to as the ramp-up period (9). Duringthe ramp-up period, traffic volumes may be significantly

lower than forecasted as drivers slowly become aware of the

toll facility and its potential for saving time and/or conve-

nience, or if population or employment growth along the

facility corridor (i.e., the potential market) is also less than

forecasted.

PERFORMANCE OF TOLL ROAD DEMANDAND REVENUE FORECASTS

Sources of Information

This section compares the projected and actual revenues forseveral facilities. However, to understand and interpret the

comparison, it is important first to understand the sources

upon which the information was based.

The projected and actual revenues were derived from dif-

ferent sources. Projections are commonly provided by the

original traffic and revenue studies for the individual facility.

The study is typically conducted several years before the

facility’s opening date, as the basis for securing funding for

the planned facility.

The actual traffic and revenue studies proved difficult to

obtain for three reasons:

• An accessible single or universal source or database of

these traffic and revenue studies does not exist. Mem-

bers of the financial community, such as bond rating

agencies, do have access to a database of financial

offerings, which include traffic and revenue studies;

however, access is available only by subscription.

Moreover, the database is not exhaustive.

• With some exceptions, facility owners generally were

not willing to provide their traffic and revenue reports,

which they considered proprietary or confidential.

• Some authorities have updated their traffic and revenue

forecasts, in the face of poor performance (projected

versus actual) and given the availability of observed

traffic and revenues. The new forecasts replace the orig-

inal study (meaning also that newer models or forecast-

ing methods may be used, as well as newer data)—that

is, a series of forecasts may be available for a given

facility. In general, the new forecast produces much

closer results in the subsequent years. Accordingly, the

authorities use the updated study as a comparison with

actual revenues, which in turn often demonstrates a

much better performance than the original traffic and

revenue study would indicate.



Other authorities prepare simplified projections of

annual revenues. These are based on an extrapolation of

the previous year’s (or years’) revenues, using growth

factors that were developed from observed growth

trends (e.g., in traffic volumes) without recourse to a

travel demand forecasting model.

Information on the “actual” revenues generally was more

readily available. Annual toll revenue statistics generally

were accessible from annual reports or directly on the owner’s

website. However, there is considerable variation as to the

amount of yearly data that each owner provides. For example,

some owners report only the most recent year, whereas oth-

ers provide information for several years. Most authorities

reported only the three most recent years, with only a few pro-

viding information for up to 10 (or more) years. That is, infor-

mation for older facilities (i.e., pre-2000) was not readily

accessible.

The different sources, and the difficulty in procuring thedifferent pieces of information, also suggest that the compa-

rability of the projected and actual revenues for a given year

may be limited. The definition of a “year” may vary between

the projection and the actual (e.g., the definition of the fiscal

year may reflect that of the owner rather than of the facility;

and some facilities may have begun operation part way

through the owner’s fiscal year).

In summary, the comparison was derived from four types

of sources:

• Comparisons of actual and projected revenues, pre-pared by various bond rating agencies.

• Financial offering statements for individual facilities,

which include the traffic and revenue projections for the

facility. These statements are circulated within the

financial community by subscription to a central com-

mercial service.

• Financial statements or reports, prepared by individual

authorities (owners). Generally, these were found on

the respective authority’s website. In most cases, only

the actual revenues were provided, although a small

number of websites also compared these with the pro-

jected revenues. Of the four types of sources, only this

one is available to the general public.• Traffic and revenue forecasts, provided by individ-

ual facility owners that responded to the survey of

practitioners.

It is important to note that several sources were used to com-

pile the information for some facilities described in this syn-

thesis. This is important for three reasons: First, as noted in the

footnotes to Table 1, in some cases the reporting methods var-

ied from year to year. Second, multiple sources of information,

and different performance results, were sometimes provided for

20

a given facility and year. Third, although the actual perfor-

mance information was provided, for some facilities the corre-

sponding projected performance was not available.

Comparison of Projected and Actual Revenues

Table 1 summarizes the performances of 26 different toll

highways throughout the United States. The table compares

the actual revenue collected as a percentage of the revenue

that was projected in traffic and revenue forecasts. The facil-

ities are listed according to the year in which the facility

opened (between 1986 and 2004). The results are presented,

where available (or where applicable; some of the facilities

opened too recently to have an established performance his-

tory), for the first 5 years of operation. The table identifies

the owner and the state in which the facility is located.

Appendix D presents brief descriptions of the individual

facilities.

It should be noted that other facilities were also investi-

gated; however, they were not included because of insuffi-

cient data and information.

Table 1 demonstrates considerable variation in perfor-

mance, ranging from a low of 13.0% for the Osceola County

Parkway in Year 1, to a high of 152.2% for the George Bush

Expressway, also in Year 1. The table also shows that there is

little consistency, as follows:

• The results do not improve with newer facilities, which

might have been expected given that the state of thepractice in modeling generally is improving. The per-

formance does not necessarily improve for a given

authority [i.e., even as a history of models and forecasts

is built up by (or for) a given authority, the perfor-

mance does not necessarily improve as a new facility is

planned].

• There is little consistency by year within a given facility,

although the performance for some facilities improves

when traffic and revenue forecasts are updated, based on

actual in-operation performance. (The most recently

opened facilities are too new to have recorded data for

any but the initial year or two).

• Most of the results demonstrate an underperformance

(actual is lower than projected), albeit with some notable

exceptions. However, the under/overperformance may

vary within a given facility by year.

• At least some of the results reflect updated fore-

casts (although the existence of updates may not have

been noted in the source material). This is corroborated

by the survey of practitioners: In response to poor ini-

tial performance, some respondents indicated that their

model was recalibrated or the model networks were

reconfigured; the demand forecasts or the revenue fore-



21

Authority/FacilityYear of

OpeningYear 1 Year 2 Year 3 Year 4 Year 5

Florida’s Turnpike Enterprise/Sawgrass

Expressway (6 )1986 17.8% 23.4% 32.0% 37.1% 38.4%

North Texas Tollway Authority/Dallas

North Tollway (6 )

1986,

198773.9% 91.3% 94.7% 99.3% 99.0%

Harris County Toll Road Authority

(Texas)/Hardy (6 )1988 29.2% 27.7% 23.8% 22.8% 22.3%

Harris County Toll Road Authority

(Texas)/Sam Houston (6 )

1988,

199064.9% 79.7% 81.0% 83.2% 78.0%

Illinois State Toll Highway Authority/

Illinois North South Tollway (6 )1989 94.7% 104.3% 112.5% 116.9% 115.3%

Orlando–Orange Expressway Authority/ Central Florida Greenway North

Segment (6 )

1989 96.8% 85.7% 81.4% 69.6% 77.1%

Orlando-Orange Expressway Authority/

Central Florida Greenway SouthSegment (6 )

1990 34.1% 36.2% 36.0% 50.0% NA

Oklahoma Turnpike Authority/

John Kilpatrick (3)1991 18.0% 26.4% 29.3% 31.4% 34.7%


Creek (3)1992 49.0% 55.0% 56.8% 59.2% 65.5%

Mid-Bay Bridge Authority (Florida)/ Choctawhatchee Bay Bridge (38,39)

1993 79.8% 95.5% 108.9% 113.2% 116.7%

Orlando-Orange Expressway Authority/

Central Florida Greenway Southern

Connector (6 )

1993 27.5% 36.6% NA NA NA

State Road and Tollway Authority

(Georgia)/GA 400 (3)1993 117.0% 133.1% 139.8% 145.8% 141.8%

Florida’s Turnpike Enterprise/

Veteran’s Expressway (3)1994 50.1% 52.9% 62.5% 65.0% 56.8%

Florida’s Turnpike Enterprise/ Seminole Expressway (3)

1994 45.6% 58.0% 70.7% 78.4% 70.1%

Transportation Corridor Agencies

(California)/Foothill North (3)1995 86.5% 92.3% 99.3% NA1 NA1

Osceola County (Florida)/Osceola

County Parkway (3)1995 13.0% 50.7% 38.5% 40.4% NA

Toll Road Investment Partnership

(Virginia)/Dulles Greenway (3)1995 20.1% 24.9% 23.6% 25.8% 35.4%

Transportation Corridor Agencies(California)/San Joaquin Hills (3)

1996 31.6% 47.5% 51.5% 52.9% 54.1%

North Texas Tollway Authority/

George Bush Expressway (3)1998 152.2% 91.8% NA NA NA

Transportation Corridor Agencies(California)/Foothill Eastern (3)

1999 119.1% 79.0% 79.2% NA1NA

1

E-470 Public Highway Authority

(Colorado)/E-470 (3)1999 61.8% 59.6% NA 95.4%

2 NA3

Florida’s Turnpike Enterprise/Polk (3) 1999 81.0% 67.5% NA NA NA

Santa Rosa Bay Bridge Authority(Florida)/Garcon Point Bridge (42,43)

1999 32.6% 54.8% 50.5% 47.1% 48.7%

Connector 2000 Association (South

Carolina)/Greenville Connector (3)2001 29.6% NA NA NA NA

Pocahontas Parkway Association

(Virginia)/Pocahontas Parkway (44,45)2002 41.6%4 40.4% 50.8% NA NA

Northwest Parkway Public HighwayAuthority (Colorado)/Northwest

Parkway (46,47 )

2004 60.5% 56%5

NA NA NA

Sources are cited in parentheses.

Notes: Bold type reflects actual within 10% of projected. NA = traffic and revenue report not available or not

provided.1For these years, the Transportation Corridor Agencies combined the revenues (earnings) for the two facilities

(Foothill North and Foothill Eastern). Accordingly, the individual performance for the two facilities cannot be

calculated.2Data reflect updated traffic and revenue study (40,41).

3Incomplete information (missing November and December).

4This is approximated owing to construction delays that only allowed the facility to be open for one-quarter

of the expected full year.

5Projected performance for the 2005 fiscal year (48).

TABLE 1ACTUAL REVENUE AS PERCENTAGE OF PROJECTED RESULTS OF OPERATION



casts were revised. Other responses included revisions

to the financial schedule, changes to the staging or tim-

ing of the project, or the implementation of annual

updates and peer reviews. On the other hand, several

respondents noted that the forecasts were accepted and

used as is (i.e., no impact).

• Even with the availability of updated forecasts, only asmall number of projections are within 10% of the actual

revenues. These are indicated in bold type in the table.

Comparison of Projected and Actual Traffic

It should be noted that Table 1 and the preceding discussion

compared projected and actual revenues, as opposed to traf-

fic. However, a multi-national review of 183 tolled and

non-tolled roads found significant inaccuracies in the traf-

fic projections as well (36 ).

Another study compared the traffic forecasts for 104tolled facilities around the world. The comparison found

considerable variability in the performance of the traffic

forecasts for the first year (during ramp-up), ranging

between 15% and 150% of actual performance. On average,

the forecasts overestimated Year 1 traffic by 20%–30%. This

“optimism bias” (error) was not reduced for subsequent years;

rather, a mixed performance profile resulted. The mean pro-

jected versus actual performance ranged between 0.77 and

0.80 over the first 5 years of operation.

The comparison disaggregated the forecasts according to

vehicle type, and found that the variability in traffic forecasts

was “consistently higher” for trucks than for light vehicles(generally, private automobiles). This reflected the greater

difficulties in predicting the trucking community’s response

to tolls, given the variability in type and size of trucking

operations. The significance is that trucks commonly pay

higher tariffs than private vehicles, meaning that their con-

tribution to revenue forecasts can be “significant,” out of pro-

portion to their volumes.

The comparison also noted the relative lack of tolled facil-

ities that are more than 5 years old. This reflects “the innova-

tive nature of the sector and that operational project-financed

infrastructure concessions are a relatively recent phenome-

non. A significant number of highway concessions globally

still remained in design or under construction” (49).

The difficulty in tracking the performance of the forecasts

over time was also noted, given “the common practice of

preparing revised or rebased forecasts for toll facilities

whose predicted use departs significantly from expectations.

In such instances, credit surveillance documentation may fail

to report the original forecasts” (49).

The analysis also compared four different forecasts for the

same tolled facility. All of the forecasts represented “base-

22

case forecasts”; that is, they were modeling the same situa-

tion but used different forecasting assumptions. The four

forecasts varied between 26% (for the Year 5 forecast) and

255% (Year 35), with a steady increase in the interim.

Very different projections of asset use result from relativelysmall divergence among the model input assumptions. . . .

Traffic forecasts, particularly in the medium to longer term,can remain very sensitive to marginal parameter changeswithin the modeling framework, even though these parametervalues are drawn from an entirely plausible range. In terms of assessing the reliability of future project cash flows, rigoroussensitivity testing clearly has a pivotal role to play in suchcases (49).

Explanation of Performance

A second study assessed the performance of all but two of

the toll facilities that are summarized in Table 1 (3). [The

two exclusions were the Choctawhatchee Bay Bridge in

Florida (Mid-Bay Bridge Authority) and the NorthwestParkway in Colorado (Northwest Parkway Public High-

way Authority)]. The performance of each was assessed in

the first 5 years of operation. All of these were start-up

facilities.

Whereas Table 1 considered the facilities chronologi-

cally, to determine whether more recent forecasts presented

any improvement in performance (as noted, no pattern was

apparent), this assessment categorized the facilities accord-

ing to several characteristics; location within the urban area,

degree of integration with the existing road network, corri-

dor income levels (i.e., the income levels of the drivers who

would use the facility), time savings offered by the facility(i.e., the extent of congestion in the competing network and

the availability of “competitive” non-tolled alternatives),

value of time (e.g., the value of time would be highest in

congested corridors traveled by high-income drivers), pro-

jected traffic growth (also related to the reliability of the

demographic and economic forecasts upon which the fore-

casts were based), and the extent of development in the area

served by the facility.

The categorization resulted in four groups, although some

overlap was noted:

1. High congestion, suburban areas;

2. Outlying roads of metropolitan areas;

3. Developed corridors, parallels of existing roads, and/

or faulty economic forecasts; and

4. Least developed areas.

The general findings are summarized in Table 2. The table

does not list the individual performances for each facility,

because the values differ from those listed in Table 1. How-

ever, the table demonstrates a decreasing performance accord-

ing to the order of the four categories. In essence, improved

performance resulted under the following conditions:



23

• Location within well-developed parts of a large metro-

politan area, with established traffic patterns.

• Location within high-income corridor, with resultant

high values of time.

• Well-connected to the road network.

• Few or no reasonable choices for non-tolled alternative

routes.

• High savings in time offered by the facility.

• Rapid driver acceptance of the new facility, with mod-erate ramp-up traffic growth and subsequently slower

growth.

• Moderate projected growth (i.e., appropriate account-

ing for economic conditions; notably, through appro-

priate consideration of the labor force, the aging of the

population, and productivity).

A third analysis of the poor performance of start-up roads

identified four types of explanatory reasons (5):

1. Model input risk, which was exemplified by the use of

regional travel demand models that had been developed

for other purposes and that assumed land use and socioe-

conomic forecasts that were appropriate for regional

planning, but were not “sufficiently conservative” to

support debt service; a “steady-state” forecast that does

not account for “the very real likelihood” of economic

fluctuations; weekend or truck traffic patterns that varied

significantly from comparable experience; and differ-

ences in actual values of time compared with estimates.

The survey of practitioners found that few travel

demand models had been created and calibrated

specifically for a toll facility study. The survey

revealed that only 15% of the models were devel-

oped specifically for the subject toll facility study

and another 31% had been calibrated for a previous

toll facility study. The remainder of the survey

respondents indicated that the selected model was

based on an existing model that had been calibrated

for other purposes. At the same time, survey respon-

dents also reported that the use of a model from a

previous toll or non-tolled study yielded the most

accurate results.

TABLE 2PERFORMANCE BY CATEGORY

Group Authority/Facility Characteristics Performance Explanation

1. Highcongestion,

suburban

Three facilities:

• State Road and Tollway

Authority (GA)/GA 400

• North Texas TollwayAuthority/George Bush

Expressway

• Illinois State TollHighway Authority/

Illinois North South

Tollway

• Well-developed urban/ suburban part of large

metropolitan area

•

Higher corridor income• Substantial corridor

traffic

• High value of time

• Good connections to

facility

• No competitive non-

tolled alternatives

• Modest projected

traffic growth

Approximatedor exceeded

projections

• Moderate toll rates

• Very rapid

adjustment of traffic

patterns followingopening

• Moderate traffic

growth in first 2–3years, then growing

more slowly

2. Outlying Seven facilities:

• Oklahoma Turnpike

Authority/John

Kilpatrick

•

Oklahoma TurnpikeAuthority/Creek

• Florida’s Turnpike

Enterprise/Veteran’s

Expressway


Enterprise/Seminole

Expressway


Enterprise/Polk

• Transportation CorridorAgencies (CA)/Foothill

North

• Orlando–OrangeExpressway Authority/

Central Florida

Greenway NorthSegment

• Less established traffic

patterns

• Less integral to the

existing network

•

These were partialbeltways

• Usually serving above-

average income areas,

but with less-established

development patterns

• Further fromemployment centers

• Moderate-to-high toll

rates (although usage

inelastic because

drivers alreadyaccustomed to paying

tolls)

Mean rangedbetween 61%

and 67% of

forecasts, on

average, with

considerablevariation

• Substantial forecast

revenue growth (35%

average over first 4

years)

• Forecast error appears

to result fromoverestimation of

initial base period

usage (high ramp-uprates)

(continued)



2. Ramp-up risk, with recent methods based on the use of

other operating facilities as proxies, but with “spotty”

results.

3. Event and political risk, for which were cited external

factors such as the unforeseen construction or expan-

sion of competing roads (San Joaquin Hills toll road),

cancellation or postponement of expected expansions

to the connecting network (Foothill Eastern), or the

inhibition of expected development (which would

have generated demand for the toll road) by a morato-

rium on servicing (Garcon Point Bridge). The slow-

down in air travel after the September 11, 2001,

24

terrorist attacks affected the forecasts for the E-470 toll

road in Denver.

Political pressures were cited as influencing fac-

tors, given that transportation authorities exist in a

political environment and this existence can depend

on the support of elected officials (25). The challenge

of evaluating projects that were generated initially

for political reasons was noted. Business motivations

were also seen as influencing factors, with politically

connected business leaders seen as generating sup-

port for toll projects that might not otherwise have

been considered (25).

Group Authority/Facility Characteristics Performance Explanation

3. Developedcorridors

Five facilities:

• Harris County Toll Road

Authority (TX)/Hardy

• Harris County Toll RoadAuthority (TX)/Sam

Houston

• Transportation CorridorAgencies (CA)/Foothill

Eastern

• Transportation CorridorAgencies (CA)/San

Joaquin Hills

• Santa Rosa Bay BridgeAuthority (FL)/Garcon

Point Bridge

• Corridors with moredeveloped or already

established traffic

patterns

• Usually constructed in

large metropolitan

areas or active tourist

areas

• “Solid” projected time

savings

• Moderate projectedrevenue growth

Mean rangedbetween 51%

and 60% of

forecasts, on

average, with

considerable

variation

• Impacts of nearbynon-tolled

alternatives

underestimated

• Overestimated time

savings

• Overly optimisticeconomic forecasts

• Failure to account for

recessions

• Overestimated

corridor growth rates

• High toll rates

• Limited history of

toll use in area

• Unusual ramp-up

problems

• Expansion of

competing non-tollednetwork

4. Least

developed

Eight facilities:

• E-470 Public HighwayAuthority (CO)/E-470

• Toll Road Investment

Partnership (VA)/Dulles

Greenway

• Osceola County (FL)/

Osceola County Parkway

• Orlando–Orange

Expressway Authority

(FL)/Central Florida

Greenway South

Segment

• Orlando–OrangeExpressway Authority

(FL)/SR-417


Enterprise/Sawgrass

Expressway

• Pocahontas Parkway

Association (VA)/

Pocahontas Parkway

• Connector 2000

Association (SC)/

Greenville Connector

• Specific traffic

generator serving as

project basis (e.g.,

airport)

• Located in

undeveloped area

• Toll road expected to

stimulate development

• High revenue growthrates

• Assumed periodic toll

rate increases

Mean ranged

between 29%

and 51% of

forecasts, on

average, withconsiderable

variation

• Insufficient existing

traffic congestion

• Overestimated time

savings or value of

time

• High ramp-up

growth rates, due to

overestimated base

period usage

• High subsequent

growth rates

Source: Muller and Buono (3).

TABLE 2PERFORMANCE BY CATEGORY (Continued )



25

Another exogenous event was the development of

competing routes or the failure to anticipate network

improvements such as feeder roads or highway inter-

changes. In some situations, noncompetition agreements

have been developed that specify that other government

agencies will not build competing facilities within a cer-

tain protected geographic area. However, the agreements

have not always been implemented.

Survey respondents cited several exogenous factors

that influenced the performance of the forecasts. All of

these concerned the actual conditions under which the

facility operated or was implemented. These factors

included the actual operations and system reliability

(e.g., actual congestion levels, operating speeds, and

incidents); impact of the tolling technology on actual

(recorded) traffic volumes (e.g., owing to unreadable

license plates); violation rate; staging of the facility (or

of other facilities); and changes in policy, mandate,

legislation, ownership, etc.

4. Model error, which reflected the inherent variability in

models regardless of how well the model was cali-

brated and validated [i.e., the forecasts can never repli-

cate the (eventual) actual traffic]. Although a model’s

average error might be small, the average “may mask

a problem, which when compounded within the model

and over time, may severely skew results. This is an

issue that is not discussed in an adequate level of detail

in traffic and revenue reports.” The analysis further

noted that the “simultaneous manifestations” of two or

more of these problems contributed further to the poor

model performance, with the forecasts “[amplifying]

the negative variance between projected and actual

traffic levels.”

Finally, the survey of practitioners found that no single

modeling factor influenced the performance of respondents’

forecasts. Respondents cited as factors the model structure;

the process used to expand the modeled time periods to

annual forecasts; the calibration process, coverage, and pre-

cision; “control” over how the model outputs were used, ana-

lyzed, or interpreted; the lack of transparency/opacity in the

modeling and forecasting process; and the validity (i.e.,

appropriateness) of the model for financing purposes.

TREATMENT OF SPECIFIC FACTORS AFFECTING

FORECAST PERFORMANCE

Drawing on the preceding discussion of the performance of

the forecasts and the underlying reasons, this section exam-

ines the treatment of specific factors that were identified as

part of the scope of the synthesis, in the literature, and by

practitioners.

Demographic and Socioeconomic Inputs

There are two relevant issues. The first concerns the use of

long-range demographic and socioeconomic forecasts (so-

called land use inputs to the model) that may reflect an MPO’s

planning policy (i.e., as the source for these inputs) as

opposed to market trends. Recent toll road demand and rev-

enue forecasts have responded to these concerns by modify-

ing these assumptions to account for input scenarios that were

more conservative and that took into account historical trends

and a more realistic assessment of likely future growth (5

).

An example is provided by a recent (2003) traffic and rev-

enue forecast for the Transportation Corridor System (the

Foothill/Eastern Transportation Corridor and the San

Joaquin Hills Transportation Corridor), in which the local

MPO land use forecasts were reviewed and refined in several

ways: an update to the forecasts according to actual devel-

opment that had occurred in the 5–6 years since the MPOs

had prepared them; a review of job and household growth

rates according to a variety of national, state, and regional

third party sources; interviews with developers, realtors, and

other related interests to identify issues that would affect

future development and the regulatory environment in thestudy area; detailed field studies of 50 “focus areas” to iden-

tify current and potential development capacity and con-

straints to development; and the identification of candidate

areas for redevelopment and infill development at higher

(than originally forecasted) rates in the long term. Forecasts

for different categories of employment were revised accord-

ing to recent trends, and forecasts for residential develop-

ment accounted for such variables as recent changes in

prices. Overall, revised short- and long-term land use fore-

casts were developed (50). The impact of the refined land use

forecasts is not yet clear, given the recentness of the study.

Although the actual revenue growth rates for 2003–2004 and

2004–2005 were greater than the projections (8.7% versus4% and 9.7% versus 4%, respectively), a July 2004 increase

in toll rates might have affected the results (51).

The second issue is the lack of consideration of the impact

of short-term economic fluctuations on travel demand. The

impact of optimistic economic projections on traffic projec-

tions was noted in several studies. The national recession of

1990–1991 affected the use of the first two segments of the

Central Florida Greenway, which had opened in 1989 with

first-year projections just slightly below actual, but with

poorer results for the next two years (over the course of the

recession). A “drag” from the recession was considered to

have affected toll roads in Oklahoma City and Tulsa, Okla-

homa, which opened just after the recession. Local economic

impacts, such as the collapse in oil prices and the subsequent

sharp regional economic downturn of 1986, left economic

growth in the Houston area well below projections, with cor-

responding impacts on the Hardy and Sam Houston toll road

revenues. Even when regional economic activity was close to

the original projections, the performance of some tolled facil-

ities still fell short, because economic activity within the

immediate corridor did not meet projections (e.g., the Saw-

grass Expressway in Florida) or the expected build-out of res-

idential areas was slower than expected (e.g., the Seminole



Expressway toll road, also in Florida) (6 ). Practitioners have

begun to consider the impact of short-term economic changes.

The aforementioned Transportation Corridor System forecast

took into account a “recession scenario,” which considered a

“double dip” of below average job gains in the immediate

term, followed by job losses for the next two years, then by a

modest recovery and a recessionary dip in the seventh year.These inputs were used as part of a sensitivity test of the

demand and revenue forecasts (50). Another observer com-

mented that “supply-driven” land use forecasts (meaning

forecasts that take into account factors such as growth in the

labor force, demographics, and productivity) provided more

stable results than did “demand-driven” inputs (such as fore-

casts of population and jobs). The pending retirement of the

“baby boom” generation was also seen to have an impact on

the demand for travel. As an example, this observer cited the

1999 Foothill Eastern refinancing study, which preceded the

aforementioned Transportation Corridor System study. This

study accounted for a more stagnant labor pool after 2010,

which in turn generated “far less” growth in the long-termtraffic and revenue forecasts (3).

A related issue concerns the ability to understand the

travel characteristics of the users of a proposed facility.

With reference to improving the performance of transit rid-

ership forecasts, one observer proposed bringing the fore-

casting horizon closer to the present, which “would reduce

the range of developments that can cause projections to go

awry, such as changes in the local economy or evolution of

travel patterns in response to geographic redistributions of

employment and population.” An “extreme variant” would

be to predict ridership under current demographic and travel

conditions, “which would isolate the increased ridershipattributable to improved transit service from that owing to

demographically induced growth in overall travel demand.”

This “opening-day” ridership would be used as the basis for

the evaluation of alternatives, rather than long-range fore-

casts (35). Although the analytical horizon for toll road

demand and revenue forecasts clearly cannot be shortened,

a current-year or very-short-term toll demand forecast based

on a hypothetical immediate opening of the facility would

allow analysts and users to differentiate the demand that

would result from the network improvement and that would

result from assumed demographic or economic growth. Fur-

ther analyses could test the impact of the facility, with and

without tolls, again in the short-term, to isolate the impactsof tolls. In other words, although these short-term forecasts

might have limited use in the development of absolute esti-

mates of revenues, they would be valuable in grounding and

interpreting the long-range forecasts (i.e., to provide a refer-

ence against which to compare that proportion of forecasted

long-term facility traffic that would use the facility whether

or not the toll is in place or independent of assumed growth).

Travel Characteristics

The availability of appropriate data and the quality of these

data were noted in the literature and in the survey as one of

26

the major sources of potential forecasting inaccuracies (25).

These data include such variables as traffic counts, network

characteristics, travel costs, land use, and employment. Inap-

propriate base year data can result in model validation errors,

which in turn affect all subsequent applications and forecasts.

In practice, these data, which are the foundation of the fore-

casts, were found to be subject to substantial numbers of mea-surement and processing errors (25). Similar problems were

found with forecast inputs such as land use: The model may

accurately reflect the assumed or calculated inputs; however,

if the assumptions are erroneous, then the accuracy of the

forecast will suffer (52).

Current data collection practices are well-established.

They include origin–destination surveys, trip diaries, activity-

based surveys, stated preference surveys, traffic counts, travel

time data, and speed surveys. Surveys of existing socioeco-

nomic and transportation system characteristics are required

for calibration. However, one observer noted that what was

once a standard part of transportation planning is not usuallyundertaken to the same degree in contemporary planning

studies (25). The Minnesota DOT model update noted earlier

used two previous studies (one local and one from California)

as sources of information, given the lack of data (26 ).

Another observer noted the need for caution in the use of

“imported” data to address gaps: “without great care and con-

siderable experience, significant errors can be introduced

into the modelling framework through inappropriate impor-

tation of model parameters.” Other factors relating to data

included the role of uncertainties and potential sources of

error introduced by sampling [in surveys]. The appropriate

categorization of travel markets in terms of their individualvalues of time and willingness to pay was also noted, with

income levels and time sensitivity (i.e., trip purpose) being

important determinants. The ability to save time was the

most important determinant of whether or not a private auto-

mobile driver chooses to use a toll road, whereas truck driv-

ers also took into account the impact on vehicle operating

costs (i.e., that a toll road’s “competitive advantage” for

trucks must be measured both in terms of time savings and

the ability to save on fuel costs and reduce vehicle wear and

tear). Similarly, the importance of “who pays” also was noted,

as was the difficulty in modeling this influence. Finally,

assumptions regarding growth in vehicle ownership (also

related to growth in Gross Domestic Product and income)

were noted as influences on traffic demand in general (53).

These findings generally were corroborated by the sur-

vey of practitioners. Respondents cited the values used for

value of time, willingness to pay, and other monetary val-

ues as influences on the performance of the forecasts. Other

influences included assumptions regarding land use fore-

casts or future network configurations, with some respon-

dents distinguishing public and political influences in these

assumptions; availability, appropriateness, or sufficiency

of the data, models, or analytical processes; environmental

or economic development considerations; and economic cli-



27

mate. Overall, respondents recommended collecting more or

better data to improve travel demand forecasting results.

Value of Time and Willingness to Pay

The treatment of the ability and willingness of potential users

to pay was cited as a key performance factor both in the lit-

erature and by practitioners. Values of time can be differen-

tiated by purpose, mode, and/or vehicle class. Willingness to

pay is a variation of value of time that accounts for how much

travelers value different attributes of the toll facility, such as

safety and reliability.

The valuation of travel time is based on two underlying

principles (54). The first principle states that time is valuable

because people can associate it directly with results, such as

making money or participating in a leisure activity—that is,

the time spent in travel could be devoted instead to other

activities. The second principle assumes that time can have

an additional cost over and above that associated with the

first principle; for example, travelers might find it undesir-

able to have to walk, wait for transit, travel on a crowded bus,

or drive in congested conditions. As a result, “the value of

saving time may vary, depending on both the purpose of

travel, which affects the possible alternative uses of time, and

the conditions under which it occurs.”

The measurement of the perceived value of a driver’s

travel time yields the value of time. This influences a driver’s

decision to use a toll road. Values of time vary from region

to region, and what is assumed for one forecast may not be

transferable to another forecast. The value of time is a func-

tion of a driver’s purpose (where work trips are more valu-able than discretionary trips), income, and personality.

The value of time is used to convert the monetary toll to

time. This allows the monetary value to be incorporated into

the model’s generalized cost function. As described earlier in

Methods for Modeling Toll Road Demand, this is incorpo-

rated into the calculation of route diversion (within or post-

trip assignment), which in turn may be fed back to other parts

of the modeling process. Two-thirds of the models reported

in the survey of practitioners incorporated value of time, 10%

used willingness to pay, and another 10% used both. One

model incorporated travel time and travel cost in its mode

choice utility equations.

The choice and derivation of the values used for this deter-

mination are the subject of considerable debate in the litera-

ture. The U.S.DOT has developed a guidance document on

the subject. Its purpose was to establish “consistent proce-

dures” for use by the department in its evaluation of travel

time changes that would result from transportation invest-

ments or regulatory actions. The guidance stated clearly that

locally derived data should be used to forecast demand on

individual facilities (54).

The guidance reviewed the factors that are associated with

the value of travel time. For trips made during work or when

the traveler could vary his or her work hours, the guidance

noted that “the wage paid for the productive work that is sac-

rificed to travel” could be used to represent value of time.

The value of time for other (personal) purposes can be rep-

resented by some fraction of the wage rate. Thus, the hourly

income (before-tax wage rates, including fringe benefits)

could be used as a “standard against which their estimatedvalue of time is measured.” As well, higher income has been

associated with higher values of time, meaning that toll roads

that operate in higher-income areas should experience

greater patronage (and support higher toll rates) (3). The

guidance developed tables that expressed the values of time

(and “plausible ranges”) as percentages of hourly incomes,

categorized by local and intercity travel, business (work-

related) or personal trip purposes, and mode (surface modes

taken together and air travel). A separate category was devel-

oped for truck drivers. A 2003 revision retained the method,

but updated the actual hourly incomes (55).

For toll road demand and revenue forecasts, the value of time generally has been assumed as a single value to represent

an average characteristic for a given study area (25). How-

ever, researchers have recognized that the use of an average

value of time masks the heterogeneity among travelers,

notwithstanding the existing categorizations of time values by

purpose. Recent research examined the preferences of users

of the SR-91 toll lanes (California) by analyzing different

revealed and stated preference survey data sets. The research

concluded that values of time and the value of reliability were

high, although the values dropped for very long distances.

However, these values contained considerable heterogeneity:

to this end, the research examined the impacts of the time of

day at which the trip was made, flexibility of arrival time, gen-der, age, household size, occupation, marital status, and edu-

cation. The research highlighted differences in the data; for

example, drivers with higher incomes were more responsive

to the toll, according to the revealed preference data (i.e.,

according to how they actually behaved), but not according to

the stated preference data. Finally, the data were used to

develop a pricing policy model, which took into account the

utilities calculated for the individual factors (as a means of

illustrating the importance of accounting for heterogeneity in

estimating the value of time) (56 ).

Other researchers identified problems in the application or

development of values of time, as compared with the findings

of empirical studies of what travelers were actually willing

to pay. These included (57 ):

• Trip assignment models that simulated route choice (i.e.,

diversion under tolls) using travel time values that had

been derived from empirical studies of mode choice.

• Application of values of time to choices whose attri-

butes were quite different from those that were used to

calculate the value of time (e.g., comfort, convenience,

and status).

• Relationships between values of time and other influenc-

ing variables (notably, income), which were assumed to



develop over time in ways that were inconsistent with

other evidence. [Other empirical studies have suggested

that the value of time increases over time, but not pro-

portionately with income levels (57 )].

• Values of time that were calculated from stated prefer-

ence methods, which must be based on very-short-term

(i.e., immediate) preference structures, but whose resul-tant values are applied to models that reflect behavior

that takes some years to evolve.

The researchers found that in cases where an average

value of time represented a skewed distribution of values,

there was a tendency to overestimate the revenue, and under-

estimate the impact of a toll, because for a given mean value

of time (i.e., for the value of time that was used in the demand

and revenue forecast) there was a smaller number of indi-

viduals who were prepared to pay the toll. To address this,

the researchers recommended the establishment of a relevant

set of purpose-specific, time value distributions; determining

a way to address these distributions in forecasting demand;“growing” the values over time; accounting for the time

values of automobile passengers (in addition to those of the

driver); and establishing methods to convert disaggregated

(heterogeneous) values of time into a single trip value that is

appropriate to the specific project under consideration.

The impact of tolls on shifts in driver behavior over time

is beginning to be understood, as a history of toll facilities

develops from which both static conditions and changes over

time can be observed. In addition to addressing the relative

paucity of data on the subject (given the relative newness of

tolled facilities), historical data are important because driver

behavior can change over time: this, in turn, affects the fore-casts that commonly retain base year rates, modeled relation-

ships, and parameter values through all horizon years. This

was demonstrated by recent research on the Lee County,

Florida, variable pricing program, which found that shifts in

traffic volumes varied over time, with the long-run (3–5

years) relative elasticity of demand being lower than that of

the short run (1 year or less). The research also found that sev-

eral demographic, socioeconomic, and travel behavior factors

affected facility use over time. Drivers who were commuting,

were full-time employees, had more persons in their house-

holds, had a postgraduate degree, and were between 25 and

34 years old were found to be more likely to have increased

their use of the facility. Retired drivers and those with lower

incomes were less likely to use the facility (58).

Two of the studies cited previously also commented on the

use of stated preference surveys as the basis of valuating time

(57 and C. Russell, personal communication, Sep. 20, 2005).

Stated preference surveys attempt to measure the value of

time by presenting hypothetical options to respondents to

quantify how toll rates would affect driver behavior. They are

widely used in toll demand and revenue forecasts. Stated pref-

erence surveys for toll demand forecasting are generally in

28

three parts: (1) background information on a recent trip in the

study corridor, (2) a set of stated preference experiments, and

(3) demographic information. The background information

provides revealed preference data about an actual trip, as well

as baseline data to customize the stated preference scenarios.

The variables of interest are determined and ordered into a

series of scenarios that is presented to respondents as part of the experiments: the scenarios are designed so as to allow the

subsequent estimation of the respondents’ relative prefer-

ences for each of the tested variables. Diversion (multinomial

logit) models and values of time in turn are calculated from

these estimates. Travel time, toll cost, and income typically

are included as attributes, with values of time calculated by

trip purpose (work and non-work) and separately for automo-

biles and trucks. Stated preference surveys in areas that do not

have tolled facilities have tended to result in low values of

time, because respondents express their “anti-toll road senti-

ment.” In areas that have existing toll roads and severe peak-

period congestion, respondents have tended to overestimate

their values of time. In either case, the calculated values of time may have to be recalibrated to reflect actual conditions

more reasonably. The availability of electronic toll collection

(as opposed to cash collection) may also influence the value

of time, in that electronic toll collection users may be less

aware of and, therefore, less sensitive to the total toll paid on

a trip (2), at least in the short term. Another practitioner has

noted that the importance of understanding what (average)

values of time derived from stated preference surveys repre-

sent; namely, they are proxies for several attributes (comfort,

safety, convenience, reliability, etc.) and that all the models

derived from these data assume that the respondent has per-

fect knowledge at the time of his or her decision making

(C. Russell, personal communication, Sep. 20, 2005).

The need for accurate ordering of preferences in the sce-

narios has been noted by some researchers, who examined the

applicability of stated preference surveys, and the methods

used to estimate values of time from them, for toll demand

analysis. The research surveyed a nationwide sample of

respondents on their preferences of 13 alternatives that

described the “essential elements of a commute,” including

congested and uncongested travel times, travel cost (usually,

a toll), and an indication of whether or not trucks were

allowed on the road. The researchers then used the resultant

preferences to calibrate different formulations of the diver-

sion model. They found that the ordering of the preference

scenarios was important and that the choice of model formu-

lation gave “very different ratings” for the same data. Finally,

the research found that the willingness to pay estimates were

relatively low and did not vary much among drivers, which

implied “that the average commuter does not appear willing

to pay much to reduce automobile travel time.” The research

noted that the distribution of willingness to pay was fairly lim-

ited, meaning that few travelers (in the sample) were willing

to pay considerably more than the average toll. The research

concluded that “extreme caution should be used in estimating

stated preferences based upon respondents’ ratings” (59).



29

Finally, one study noted the “growing body of empirical

evidence that travelers value reliability as an important factor

in their tripmaking decision.” Reliability generally reflects the

day-to-day variability in expected journey times, owing to

nonrecurrent congestion such as incidents, weather, construc-

tion, and so on. Reliability is considered important in variable-

priced HOT applications, where tolls are adjusted accordingto traffic volumes, to maintain a specified level of service.

Therefore, average travel times on HOT lanes may be only

slightly reduced from those on non-toll lanes; however, the

day-to-day fluctuations in travel time variability are reduced

significantly. Reliability can be critical for travelers with fixed

schedules (such as individuals with daycare pick-ups or those

going to the airport), and is not necessarily correlated with the

traveler’s general value of time. However, there remain few

(if any) operational demand models that account for reliabil-

ity in traveler values of time or that measure the value of reli-

ability, because of a general lack of data: this reflects the few

examples of operational toll roads using variable pricing from

which empirical data can be drawn (29).

Tolling “Culture”

An international review of toll road traffic and revenue fore-

casts demonstrated better performance for countries that had

a “history” of toll roads, compared with those for which road

tolling was new. In “countries with a history of tolling, con-

sumers can be observed making choices about route selec-

tion, effectively trading off the advantages against the costs

of using tolled highway facilities. The consumer response

can therefore be more readily understood by forecasters

preparing predictions for new or extended facility use.” Incontrast, in countries where tolling is new, there are no

revealed preference data on consumer behavior, which

“leaves forecasters more reliant on theoretical survey tech-

niques and assumptions about how drivers may respond to

tolls” (60). The existence of a tolling “culture” also affected

the ramp-up forecasts, with there apparently being “little

transferability of experience between projects (particularly

those in different countries). Ramp-up tends to be project-

specific” (53).

Other influences on traveler choice included the conve-

niences that toll roads offered or were seen to offer.

Improved safety was cited as an influence that would attractdrivers to a “perceived” safer highway, such as one with

fewer trucks (61). Commuters also valued reliability, and

travelers may be willing to pay for predictability of travel

time, especially for time-sensitive activities. The type of toll

collection was cited as a third influence on drivers’ route

choice (61).

Truck Forecasts

The treatment of truck forecasts was the subject of an analysis

that found that the variability associated with such forecasts

was consistently higher than that for light vehicles. Although

truck traffic typically comprises a relatively small portion of

the traffic mix, they commonly pay two to five times and

sometimes as high as 10 times the respective car tariff and so

their contribution to total revenues can be significant. The

choice by trucking firms to use toll roads was also found to

depend on, among other factors, the size of the firm, with inde-pendent owner–operators (that dominate some trucking sec-

tors) being “very sensitive” to tolls (49).

Ramp-Up

The ramp-up period reflects a toll facility’s traffic perfor-

mance during its early years of operation. This period may

be characterized by unusually high traffic growth. The end of

the ramp-up period is marked by annual growth figures that

have (or appear to have) stabilized and that are closer to traf-

fic patterns that have been observed on other, similar facili-

ties. The ramp-up period reflects the users’ unfamiliarity witha new highway and its benefits (“information lag”), as well

as a community’s reluctance to pay tolls (if there is no prior

tolling culture) or to pay high tolls (if there is a history). The

performance of the facility during ramp-up is particularly

important to the financial community, because the “proba-

bility of default is typically at its highest during the early

project years” (9).

As the initial year forecasts in Table 1 indicated, the ramp-

up performance has been problematic and inconsistent. One

analyst noted that the consideration of ramp-up forecasts had

three dimensions (9):

• Scale of ramp-up (i.e., the magnitude of difference

between actual and forecasted traffic).

• Duration of ramp-up (from opening day to beyond

5 years).

• Extent of catch-up [i.e., having experienced lower-than-

projected usage at the time of the facility’s opening, to

what extent could observed traffic volumes catch up

with later year forecasts? The significance of catch-up

volumes, according to one analyst, was that “projects

with lower-than-forecasted traffic during the first year

operations also tend to have lower-than-forecasted traf-

fic in later years (referring to toll and non-tolled roads,

as well as transit and inter-urban rail) (62)].

In other words, the conditions that define ramp-up were spe-

cific to each project, given that the factors that influenced

ramp-up also varied (e.g., signage and marketing, the tolling

culture, and the availability of competing free routes).

Another analyst noted that until recently ramp-up has

largely been ignored. Subsequent efforts have considered

ramp-up; however, these have tended to be based on the use

of other facilities as proxies. However, as more facilities

have opened, actual operations and influencing factors could



be observed. An inverse relationship between time savings

and ramp-up has been observed, such that greater time sav-

ings appeared to correlate to a shorter ramp-up (5). As an

example, another analyst noted that the estimates of time sav-

ings for the Hardy and Sam Houston toll roads in the Hous-

ton, Texas, area were very different, with the Hardy saving

less than 5 min per average trip and the Sam Houston savingmore than 10 min. “At some level, this would indicate that

the basic need for the Sam Houston was probably greater”

(which was demonstrated by the significantly better perfor-

mance of the Sam Houston toll road; see Table 1). Also,

income levels along the Sam Houston corridor generally

were higher than those in the Hardy corridor (meaning that

the value of time would be higher for the former) (6 ).

In the absence of extensive observations, however, one

analyst proposed the use of “revenue-adjustment” factors for

ramp-up forecasts. These varied according to the expected

duration of the ramp-up period, the extent to which the ini-

tial observed volumes would have to catch up (i.e., whetherthe initial volumes were less than projections, and by how

much), and the source of the toll demand and revenue pro-

jections (meaning that those commissioned by banks

appeared to be more accurate than those of others, such as the

sponsor; with the former group “typically referred to as ‘con-

servative’ forecasts” and used as the base case in financial

analyses). The factors were derived from a comparison of the

performance of 32 tolled facilities around the world. The fac-

tors ranged from a 10% reduction to first-year revenues for

facilities with a two-year ramp-up and no catch-up required,

according to a bank-commissioned forecast, to a 55% reduc-

tion to first year revenues for facilities with an eight-year

ramp-up and initial volumes 20% less than projected, accord-ing to forecasts commissioned by others (9). However, it

should be noted that although they mask considerable varia-

tion, the averages of the actual versus projected results listed

in Table 1 improve over time, from 59% in the first year to

70% in the fifth year.

An alternative treatment was to recognize the potential for

an extended ramp-up by accounting, in the forecast, for the

market served by the facility; the duration was assumed to be

extended if the facility was “development dependent” and

could also be shortened if it was in a built-up area. The start-

ing point (base forecast) also could be reduced according to

experience on other nearby facilities. This allowed the

“inability to accurately predict a key factor [to be] balanced

by very conservative assumptions” (5).

Time Choice Modeling

The need for models that more accurately capture the dif-

ferences in travel patterns by time of day, day of week, and

even season was identified by the financial community. The

object is twofold: first, to account more explicitly for the

temporal variation in composition of trip purposes, origins

30

and destinations, and vehicle types, including (in addition to

peak-hour travel) the off-peak, midday, night, and weekend

(5). This would replace the common use of factors to expand

peak-hour models to daily and then annual traffic volumes

for purposes of revenue forecasting. It also would replace

the use of 24-h models, from which estimates of hourly traf-

fic volumes must be derived (typically using factors) as thebasis for forecasting diversion under tolls; some MPO mod-

els, which are used as the basis for toll road demand and rev-

enue forecasts, simulate only daily traffic; see, for example,

“Tyler Loop 49—Level 2 Intermediate Traffic and Toll

Revenue Study” (63). Research on the impacts of variable

pricing in Lee County, Florida, found that shifts in trip start

times changed over time; that is, long-run elasticities of

demand were lower than short-run elasticities (58).

The second object is to add and integrate time-of-day

choice with mode and route choice. The Florida Turnpike

Enterprise provides an example of how time-of-day choice

was integrated with modal and route choice. It has relied onwhat it considered best practice in its toll-forecasting proce-

dures for periodic updates of traffic and revenue forecasts, as

well as for the planning, design, and economic feasibility

assessment of proposed new facilities (33). A basic nested

logit approach was used to describe travel behavior for mode,

route, and time of day, with the following choices (33):

• Mode: automobile, drive alone; automobile, two occu-

pants; automobile three plus occupants; bus; and rail.

• Route: tolled or non-tolled roads.

• Time of day: desired travel time or time-shifted trip.

Sixteen statistically estimated nested modal choice mod-els were developed from survey data. These models incor-

porated four time periods and four trip purposes. The models

also included specific decision tree hierarchies for transit and

occupancy classes. Four specific time periods were modeled:

a.m. peak, p.m. peak, midday, and nighttime.

Risk

The need to address risk and uncertainty more comprehen-

sively was cited by the financial community and by

researchers. At the same time, the survey of practitioners

indicated that only a small number of respondents conducted

a risk assessment, with most of the remainder verifying their

results through judgment or reality checks and others not

doing any verification.

Many assumptions and variables must be interpreted and

relied on to complete a traffic and revenue study. The ability

to ensure exactness and accuracy in all of these is limited for

representations of existing conditions as well as forecasts. A

common treatment has been to address uncertainty through

the simple use of conservative assumptions or ranges. How-

ever, this was not always possible, as indicated by some



31

survey respondents, who noted the impact on the accuracy of

forecasts of exogenous factors such as public or political

inputs, land use, and network assumptions. For example,

when uncertainties existed about whether a particular com-

peting road would be constructed, it had been the practice to

conservatively assume the competing road would be com-

pleted if it was expected to have a negative impact on the tollfacility. If it was not expected to affect the toll project, it was

then assumed not to be in place (61).

The literature indicated that sensitivity analyses on key

variables was common practice, such as the area growth rate,

value of travel time, planned toll rates, and other variables

that were region-specific or that had shown a high degree of

variability in the past. The SR-520 Toll Feasibility Study for

the Washington State DOT provides an example. The pur-

pose of the toll feasibility study was to determine the revenue

potential and traffic impacts of tolling a replacement bridge

on SR-520. To quantify uncertainty, two tolling objectives

were modeled to “bookend” the upper and lower bound of the reasonable toll possibilities within the corridor (64).

However, respondents to the survey of practitioners recog-

nized that sensitivity analysis might not be sufficient,

because just over half of the respondents stated the need to

conduct more risk assessment in the forecast process. A risk

analysis process can evaluate thousands of different scenar-

ios to quantify the probability of a “range of potential out-

comes” (65).

In other words, it is important, first, to ensure that risk

assessment is incorporated explicitly into the forecasting

process and, second, to make the distinction between the

assessment of risk and other indirect treatments of uncer-tainty (such as judgment or sensitivity analysis). The first is

related, in part, to the inclusion of an appropriate and com-

plete set of assumptions and inputs, and in part to ensuring

that model makers and users of the model outputs all under-

stand the implications of alternative choices or influences

(whether qualitatively or quantitatively).

The second requires a proper understanding of the roles of

the different treatments of uncertainty. For example, some

financial analysts have commented that sensitivity analysis

does not adequately reveal the range of possible outcomes in

a toll road forecast (65). Instead, a range of possible out-

comes could be explored, based on Monte Carlo simulation

and the probability not only of the variables acting as indi-

vidual occurrences but in combination with each other based

on their respective probability of occurrence (65). Another

treatment is offered through “reference class forecasting,”

which uses the experiences of past projects to help statisti-

cally identify the probability of given inputs occurring at a

particular value (36 ).

Toll road demand and revenue forecasts have given little

or no consideration to the possibility of a series of events

occurring simultaneously (65)—for example, if economic

growth recedes, oil prices spike, and a large development that

was scheduled to be in place at the time of the opening of the

toll road is cancelled. Traditional sensitivity analysis typi-

cally took each of these assumptions and varied them one at

a time; however, these assumptions often varied by arbitrary

amounts. A further problem was that in reality, rarely, if ever,

did these assumptions vary from actual outcomes one at atime (65).

Financial analysts have noted that the simultaneous occur-

rence of several vulnerabilities contributed to toll roads gen-

erating lower-than-expected traffic levels and, accordingly,

toll revenues (5). Although risk analysis has been incorpo-

rated into some analyses, one analyst noted that many project

uncertainties were external to the traffic model environment.

Because these uncertainties may not be fully captured in the

model probability analysis, the model outputs must also be

interpreted within the framework of the risk analysis (60).

The National Federation of Municipal Analysts (NFMA)is comprised mainly of research analysts, who are responsible

for evaluating credit and other risks with respect to municipal

securities. NFMA has worked with nonanalyst professionals

in various sectors to develop recommended best practice

guidelines for certain markets, including the toll road demand

and revenue forecasts. In these guidelines, NFMA has

attempted to account for the likelihood that there are many

possible outcomes if future events do not follow the projected

assumptions that are predicted in the model. Given the large

number of input variables required in the modeling process,

NFMA found that the results of forecasts can be significantly

influenced by changes in these inputs (65). Simplistically, by

applying the appropriate background data inputs to the tollforecast, a model could produce a traffic and revenue forecast

that is most likely to occur, often called a base scenario.

Whereas a single best statistical estimate may be desired by

some, there are limitations to a single expected outcome.

A proposed mitigation strategy is the assignment of a

probability distribution to all inputs, or at least to those inputs

identified as most influential to the process. Each individual

variable, with its own probability distribution, can be fluctu-

ated simultaneously. This capability supports a better

approximation of reality then can be obtained, because in

practice variables do not generally change one at a time but

concurrently with varying rates of change (65).

NFMA’s best practices guidelines developed a disclosure

requirements list with respect to creating a range of possible

outcomes for traffic and revenue studies. The list included

the following:

• Creation of a no-build traffic forecast (including truck

and congestion analysis) for the study area, without the

toll road.

• Creation of a baseline traffic and revenue forecast (as

per standard practice).



• Sensitivity analysis while simultaneously varying toll

road inputs simultaneously (the following list is a guide

only, but it should be used as the minimum standard):

– Population growth,

– Employment growth,

– Personal income growth,

– Toll elasticity by consumers, and– Acceleration of planned transportation network.

• Debt service analysis with toll road project sensitivity

analysis.

A risk analysis using the Monte Carlo technique was

applied to several factors that were used to develop traffic

and revenue forecasts for a proposed toll road linking Hong

Kong with nearby industrial areas in southern China. The

risk analysis found that whereas variations in the forecasts of

population had insignificant impacts on the traffic and rev-

enue forecasts, the impacts of variability in the trip genera-

tion were very large, with standard deviations of the forecasts

being of the order of double (or more) than the base forecasts.Variations in the estimated diversion rates to the facility and

in the toll rate also were found to be significant. From these

findings, the authors pointed out that the impacts of variabil-

ity and uncertainty in these factors can influence the traffic

and revenue forecasts, and noted that “if the effects of varied

key assumptions and scenario options are not examined, the

real optimal rate of return could be missed” (66 ).

One risk analysis consultant noted that the determination

of risk should not focus on a single outcome but should

explore a range of possible outcomes (D. Bruce, personal

communication, March 4, 2005). This process first deter-

mines the degree of risk in each input variable by developingprobability distributions for all variables. Risk analysis is

carried out by allowing all the underlying variable estimates

to vary simultaneously, which can be done using simulation

techniques such as the Monte Carlo technique. The risk and

uncertainty in the underlying input variable is then translated

into a probabilistic, risk-adjusted forecast of output variables

such as traffic levels, toll rate, revenue, and debt service cov-

erage. Finally, the variables that drive risk, that is to say the

variables that have the greatest influence on the forecast, are

identified.

Bias

The existence of “optimism bias” in transportation projects

has been noted by some observers. “It is in the planning of

such new efforts [referring to projects in a city ‘for the first

time,’ where ‘none existed before’] that the bias toward opti-

mism and strategic misrepresentation are likely to be largest”

(36 ). Another analysis noted the influence of bias on the per-

formance of forecasts, with “systematic optimism bias” cited

as a “distinguishing feature of toll road forecasts.” The analy-

sis recommended that “base case forecasts should be

adjusted to take account of any suspected optimism bias.”

32

Bias was differentiated from “general error” in modeling,

with the performance of traffic forecasts for tolled and non-

tolled roads generally being “very similar.” The update also

suggested the need to consider distributions in error (67 ). On

the other hand, in a project for which multiple bids are

assessed competitively, the forecasts that are used as the

basis of financing, and which are available to the financialcommunity, are those of the winner: the forecasts for the

unsuccessful bids generally will project lower revenues

(D. Johnston, personal communication, Aug. 17, 2005).

The literature review uncovered no formal methods or

guidance to address optimism bias in toll road demand and

revenue forecasts. However, the British Department for

Transport recently developed a guidance document on pro-

cedures to address optimism bias in transportation planning

(68). Although this document addressed the cost side (for

both roads and public transport), its approach could inform

any future discussion of optimism bias in demand forecast-

ing. The guidance document noted that transportation proj-ects always must be considered “risky,” as a result of long

planning horizons and complex interfaces. It was theorized

that optimism bias resulted from a combination of the struc-

ture of the decision-making process and how the decision

makers were involved in the process. Optimism bias was not

an unknown or imaginary phenomenon; rather, it was a log-

ical product of the participants involved, their interests, the

framework for conditions for funding, and the resulting

incentive structure they encounter.

The causes of optimism bias were grouped into four cat-

egories: technical, psychological, economic, and political.

Technical causes included the long-range nature of the plan-ning horizons and that often the project scope and ambition

level can change during the development or implementation

of the project. Psychological causes were explained by a

bias in the mental make-up of the project promoters and

forecasters who can all have reasons to be overly optimistic

in the approval stage—for example, engineers want to see

things built and local transportation officials are keen to see

projects realized. Economic causes were demonstrated by

the argument that if the project went forward and was imple-

mented, then more work was created for the industry; and if

the participants were involved directly or indirectly, there

could be an affecting influence. Finally, political causes

were seen as influencing the perceived optimism bias in

terms of interests, power, and the prevailing institutional

setting that surrounded decision making on transportation

projects.

Project appraisers were seen to have demonstrated a ten-

dency to be overly optimistic. To address this bias, apprais-

ers should make explicit, empirically based adjustments to

the estimates of a project’s costs, benefits, and duration.

These adjustments should be based on data from similar past

projects and adjusted for the unique characteristics of the

project.



33

Optimism bias uplifts were introduced as methods of

combating optimism bias in the decision-making process.

They were established as a function of the level of risk that

the British Department for Transport was willing to accept

regarding cost overruns in transportation projects. The gen-

eral principle was that the lower the level of acceptable risk,

the higher the required uplift. The optimism bias upliftsshould be applied to estimated budgets at the time of the deci-

sion to build.

To minimize optimism bias, preliminary findings indi-

cated that formal and informal rules aimed at changing the

established culture should be applied. The following four

benefits to applying optimism bias uplifts to budgets were

identified:

• Emphasis on establishing realistic budgeting as an ideal

while eliminating the practice of overoptimistic bud-

geting as a routine.

• Introduction of fiscal incentives against cost overruns.• Formalized requirements for high-quality cost and risk

assessment at the business case stage.

• Introduction of an independent appraisal process.

Model Validation

Model validation and model calibration are not the same:

Calibration demonstrates how the model (and its individual

components) replicates observed historical data, whereas

validation proposes to demonstrate how “reasonably” the

model’s functional forms and parameters predict actual

observed behavior. It was noted earlier (Explanation of Per-formance) that the inherent model error in toll road demand

forecasts was not addressed, and often masked, by existing

model validation that demonstrated base year forecasts

“near 100% to actual traffic” (5). A thorough evaluation of

toll road demand and revenue forecasts was found to com-

prise three steps: technical quality and merits of the fore-

casting process, interpretation and professional judgment on

forecast results, and risk analysis and sensitivity analysis on

key input variables (25).

A “major criticism of transportation demand models is the

general lack of concern for, and effort put into, the validation

phase of [model development].” Time, budget, and data con-

straints in “typical” practice contribute to this lack of concern;

however, “the improvement in predictive capabilities of

transportation demand models and in the credibility of these

models with decision makers, rests to a large extent on the

analyst’s ability to validate the procedures used.” Three gen-

eral approaches to model validation are described here (12):

• Reasonableness checks of parameters and coefficients—

for example, checking whether or not a particular value

is within an expected range or has the correct sign. The

objective is threefold: to ensure that the model does not

violate theoretical expectations and, if it does, to iden-

tify the source of the error (the model or the expecta-

tion); to ensure that the model does not exhibit any

“pathological tendencies”; and to ensure that it is inter-

nally consistent (meaning that its outputs do not violate

any assumptions used to generate them).

Reasonableness checks of the model outputs also canhelp the user determine whether the forecasts are reason-

able. For example, the projected market for a proposed

tolled facility should be verified to ensure that it is rea-

sonable. Readily available techniques, such as a select

link assignment, could be used. [This technique isolates

which travelers (as measured by trip origins and destina-

tions) are projected to use a particular facility. The tech-

nique then could be used to determine how this subset

of travelers would behave under changes in network

configuration—for example, with or without tolls, or

with or without the facility in place]. However, one con-

sultant noted that many traffic and revenue studies do not

always include this (simple) verification, or a demon-stration of the time advantages offered by the proposed

facility (C. Russell, personal communication, 2005).

• A rigorous test of a model’s predictive capabilities is

provided by using the model to predict demand for a

time period other than that used for the model cali-

bration; this assumes the availability of (at least) a

second set of observed data. The use of the model to

predict some historical condition can be instructive;

for example, in identifying the need to better account

for unforeseen influences (such as conditions of eco-

nomic stagnation).

• When data for multiple time periods are not available, an

alternate (but more restricted) test involves the randomsplitting of the “one-period” data into two sets. One set

is used for calibrating the model, which is then used to

predict the second set’s demand. This allows the model’s

predictive capability to be validated against an indepen-

dent set of data, although the validation is limited by the

lack of temporal difference between the two sets.

The temporal, budgetary, and data constraints posed to

model validation were cited by another traffic and revenue

consultant as fundamental reasons for inaccuracies in toll

road demand and revenue forecasts. In particular, basic data

were often old, incomplete, or unreliable because of limits to

sample size; in turn, this implied that the models that were

developed from these data were subject to “substantial error”

(D. Johnston, personal communication, Aug. 17, 2005).

Peer Reviews

Peer reviews are processes in which external experts (i.e., in

modeling) can provide technical guidance and advice to the

proponent’s team during the course of the development of the

data, models, and forecasts. Peer reviews have been a part of

the toll demand and revenue forecasting process in the past,



but on a very limited basis and at a very small scale (5).

Accordingly, the value of the peer review process has been

somewhat limited to date, because the process must be con-

tinuous to show any improvement or advancement of tech-

niques. To benefit from this process, detailed reports must be

prepared and independent meetings conducted with bond rat-

ing agencies to discuss the extent of the review and theresults achieved (5).

The requirements vary for peer reviews for models in U.S.

transportation planning practice. The approaches of FTA,

FHWA, and TMIP are described here.

• FTA has specific requirements and standard review

procedures for forecasting as part of its New Starts

discretionary grant program. These review procedures

were implemented to enable FTA to evaluate and rate

grant applications on an unchanging, unbiased level.

Projects seeking New Starts funding must first pass a

locally driven, multi-modal corridor planning process,which has three key phases; alternatives analysis, pre-

liminary engineering, and final design. The list of

evaluation criteria includes operating efficiencies

(e.g., operating cost per passenger mile) and cost-

effectiveness (e.g., the incremental cost per hour of

transportation system user benefits) (69).

• FHWA allocates funding on a formula basis; meaning

that states and MPOs do not have to compete on a

project-by-project basis for funding. As a result, the same

degree of standardization is not required of forecasting

procedures as in FTA’s New Starts program. However,

FHWA does provide technical assistance to state DOTsand to MPOs in an attempt to ensure that the travel

demand forecasts used are credible and based on proper

planning practice. Three types of assistance are provided

(B. Spears, personal communication, Aug. 18, 2005):

– A checklist of questions that help FHWA, state DOT,

and MPO staff understand what is required of the

model. FHWA staff is encouraged to ask these ques-

tions during the triennial certification reviews that

are conducted with those MPOs with populations of

more than 200,000.

– The creation of a peer review team, whose responsi-

bility is to review the forecasting process and make

recommendations for improvement. These teamstypically include from four to eight travel modelers

from other agencies, MPOs, state DOTs, academia,

or consulting firms, who meet with local planners for

one or two days of review.

– The revision of travel forecasts used in specific proj-

ect documents, such as transportation plans, confor-

mity determinations, and environmental studies.

If a peer review is convened, then several pieces

of information and documentation must be made

available to the team, including an inventory of the

current state of transportation in the area, key plan-

34

ning assumptions used in developing the forecasts,

and descriptions of the methods used to develop fore-

casts of future travel demand (70).

• In addition to its core methodological research, TMIP

reviewed ways to improve existing modeling processes.

To this end, a peer review panel recommended several

types of improvements to the practice of travel demandmodeling, including the peer review process. The rele-

vant recommendations are summarized here (71):

– Land use should be integrated into multiple stages of

the travel demand model, and the integration of land

development patterns into the models is paramount.

– Understanding and incorporation of freight-based

activities into travel demand modeling, because it can

require specialized surveys, and their effect on traffic.

– Migration to activity-based or tour-based modeling

to be conducted only with ample funding, resources,

and proper documentation; otherwise, it is recom-

mended to improve the existing trip-based models.

– Agency creation of coordinated data collectionstrategies and standardization guidelines for regional

modeling.

– Improvement of data quality by supplementing

existing data sources with specialized add-on sur-

veys. This can help to complete data sets that are

incomplete as a result of low survey participation or

inadequate funding.

– Consistency checks undertaken throughout the mod-

eling process.

– Model should be designed to be flexible enough for

a variety of toll and HOV modeling policies to be

evaluated.

– Micro-simulation modeling can be used for moredetailed modeling of areas that have unusual charac-

teristics or that are highly diverse.

– Consideration of time-of-day variables, despite cur-

rent modeling difficulties.

– Agency pooling of resources and sharing of their

experiences through best practice publications.

– High-quality documentation of the components of

and assumptions in agency and region travel demand

models.

One example of the successful use of a peer review, as part

of an overall process, was provided by a toll authority that

responded to the survey of practitioners. This authority has

used its travel demand model for several studies, including

feasibility, policy, investment-grade forecast, design, review

or audit, and state environment analysis studies. For the

analysis of the toll facility feasibility study, the existing model

was updated and enhanced. The success of the forecasting has

been attributed to the regular refinement of the MPO model

(upon which its model is based) over a 10-year period. The

demand and revenue forecasting process has a built-in critical

peer review process. The toll authority has taken a proactive

approach in updating and enhancing a specific aspect of the

model on an annual basis; revalidating the model annually



35

using traffic counts, origin–destination surveys, speed and/or

travel time surveys, and land use inputs and network charac-

teristics. The toll authority noted that it has recently received

a rating upgrade on its revenue forecasts.

There are no formal requirements for peer reviews in toll

road traffic and revenue forecasts, although the bond ratingcommunity has called for more and improved reviews (5).

Financial backers do conduct their own “stress tests” of the

revenue and financial forecasts. In addition, as part of its

project oversight and credit monitoring, The Transportation

Infrastructure and Innovation Act, the federal credit assis-

tance program for major surface transportation projects,

requires a project’s senior debt to have the potential to

achieve an investment-grade bond rating. It also requires the

development of an ongoing oversight and credit monitoring

plan for each project, which includes a risk analysis, and

requires that traffic and revenue forecasts be updated and

monitored (72).

In comparison, the general practice in Europe is to con-

duct three sets of forecasts: the grantors of the concession

(the governments); the facility sponsors (proponents); and

the financial backers (lenders, investors, and/or auditors)

(C. Russell, personal communication, 2005). The govern-

ments’ forecasts are normally considered to be overly opti-

mistic, because they are used to develop long-range policies

and plans. The proponents’ forecasts are usually the most

extensive, although they do not always provide the best

results because they are dominated by the model. The finan-

cial forecasts (audits) are usually smaller efforts that are

intended to review the proponents’ forecasts (although more

substantive efforts may follow if the review identifies funda-

mental problems). The audit relies on sensitivity tests,spreadsheet modeling, and stress testing: at the end of the

process, the auditors also must assume responsibility for the

forecast.

The proponents pay for their forecasts as well as those of

the financial backers. As a result, the proponents try to con-

trol the latter’s audit. Some proponents now bring the audi-

tors into the process early, before the lenders are appointed;

this puts some pressure on the auditors, but also provides an

opportunity for them to suggest improvements early in the

process and—by the proponents who have put forward their

case—allows issues generally to be understood.

Conversely, in some places the use of two or more inde-

pendent forecasts for proponents “has been dropped because

(at double the cost) it confuses the audience,” with the result

that—in a “tight bidding timeframe” much effort is wasted

determining who is right, rather than exploring the key risk

issues and refining one approach (D. Johnston, personal

communication, 2005).



This chapter identifies checklists and guidelines that could

be used to improve the state of the practice in toll demand

and revenue forecasting. Whereas the preceding chapter

reviewed practices in specific topics, the checklist and

guidelines could provide a framework within which these

topics can be addressed. Three examples are taken from the

literature. They comprise a checklist, guidelines, and an

index: the checklists and guidelines constitute lists of ques-

tions and issues that should be addressed in a toll road

demand and revenue forecast, whereas the index provides away to organize and understand the factors and parameters

that influence the forecast. All are aimed at helping the facil-

ity owner, proponent, and financial analyst (and their mod-

elers) to better understand the process and identify questions

that should be asked in the development of toll road demand

and revenue forecasts.

CHECKLISTS AND GUIDELINES FOR MODELS

Federal transportation planning legislation requires that each

MPO develop a transportation plan as part of its planning

process (70). A transportation plan requires forecasts of future demand for transportation services that are usually

arrived at by using travel demand models. Of specific inter-

est to the toll demand and revenue forecasting community

should be the documentation and access to the planning

assumptions and forecasting methods used in the travel

demand modeling process. FHWA has compiled a checklist

for travel demand forecasting methods, mainly with the pur-

pose of providing a certification review team with an

overview of travel forecasting methods used by MPOs. Spe-

cific examples of important planning assumptions included

in the checklist are:

• Population change (should be compared with past

trends and with statewide demographic control totals).

• Employment change (should be compared with past

trends and with statewide economic growth control

totals).

• Regional distribution of future population, employ-

ment, and land use.

• Demographic change (including automobile owner-

ship, household income, household size, and multi-

worker households).

• Travel behavior change (including telecommuting, trip

chaining, and Internet shopping).

36

Specific examples of important forecasting methods

included in the checklist are:

• Last model revision (i.e., when were new variables,

new algorithms recalibrated with new data?).

• Model specification (i.e., choice of model, specification

of key model coefficients).

• Calibration data (what data were used; e.g., National

Household Travel Survey or Census Transportation

Planning Package).• Local survey (how was the survey conducted, what type

of control?).

• Model validation (what year and data source was the

model validated against?).

• Size of network.

• Number of zones.

• Non-home-based travel (e.g., freight commercial ser-

vices and tourists).

One analyst identified a list of recommended enhance-

ments, from the perspective of the financial community,

which would make the results more acceptable and more

likely to qualify for investment grade status. The recommen-dations included (5):

• Incorporation of a range of possible outcomes given the

low probability that the base case forecast will exactly

match the likely outcome.

• Further study and greater validation of value of time as

an input in forecasting models.

• Further study and greater validation of the ramp-up

effect on start-up toll road facilities.

• More detailed truck traffic analysis as the higher rev-

enue margin created by trucks is an important compo-

nent of a forecast, especially when trucks are projected

to be a significant fraction of traffic.

• Incorporation of the risk and reward of electronic toll

collection with respect to violations and toll evasion

against faster throughput, ease of use, and revenue

recovery through penalties.

• Enhance the investors understanding of the exposure to

modeling while highlighting risk in the final product (i.e.,

enhancing the validation process by validating more than

one year, full disclosure of model limitations, etc.).

In a review of the performance of 14 toll road projects,

another financial analyst identified the key variables and

CHAPTER FOUR

CHECKLISTS AND GUIDELINES TO IMPROVE PRACTICE



37

inputs that he believed have had large repercussions, both

positively and negatively, on toll road demand and revenue

forecasts (6 ).

One key variable was economic activity. Although

national economic trends were relevant, the economic activ-

ity within the region and project corridor had a greaterimpact. An example of this is in Harris County, Texas, where

a sharp downturn in oil prices in 1986 left economic growth

in the region well below the projected levels. Particularly

hard hit was the downtown business district, the primary end

destination for many vehicles that were projected to use the

toll road, which caused the forecasted traffic demand to be

considerably lower than was projected.

An input that was constant in all the successful forecasts

was the use of 30% or less as projected revenue growth

over the first 5 years of operation. This low projection

explicitly captured the high initial demand of the road with

no need for toll increases. In contrast, where a very highrevenue growth rate was assumed at the start of the proj-

ect, it indicated a dependency on the growth of these routes

to meet the forecasted revenue. If these growth rates were

not met, the lost revenues were not easily recaptured in the

following years; indeed, the forecast continued to lag

behind initial projections.

Other inputs and variables that appeared most crucial in

the model forecasting process were time saved, the cost of

travel, and the ability or willingness to pay of potential users.

In summary, the most successful forecasts generally had

accurate or even conservative economic forecasts with mod-

erate anticipated growth levels. These toll roads were built incorridors that were fully developed and where congestion

already existed. Another factor was that the corridor income

levels were above the regional levels and time savings were

in excess of 5 min.

GUIDELINES SPECIFIC TO TOLL ROAD

FEASIBILITY STUDIES

The Texas Turnpike Authority (TTA), a division of the

Texas DOT, provides an example of recommended guide-

lines for conducting traffic and revenue studies in support

of toll feasibility analyses (73). The key goals of the guide-

lines were to outline the traffic and revenue reporting

requirements, improve the consistency of assumptions, and

improve quality assurance. Four levels of analysis were

proposed (74):

• Conceptual—determines the potential for a toll road

project to support bonds. (Expected durations of each

type of study were provided and they are listed here as

an indication of the level of effort and detail. The con-

ceptual level had an estimated duration of 1–4 weeks.)

• Sketch—project-specific estimate of costs, demand,

and revenues (6 weeks duration).

• Intermediate—refines the previous analysis, including

a tolling plan. It is expected that demand projects would

be derived from a travel demand model (4–6 months

duration).

• Investment grade—“extensive and detailed” analysis

“to determine its value in anticipation of proceeding to

the bond market” (12–18 months duration).

The guidelines recommended a forecast period of 40

years beyond the opening date of the project. The following

inputs should be taken into account (73):

• Average daily tolled and non-tolled volume,

• Weekday toll transactions,

• Gross annual revenue,

• Tolled length,

• Number of lanes being tolled,

• Truck percentage,

• Opening year automobile and truck toll rate,

• Toll increase increment,

• Period between toll increase, and

• Equivalent revenue days.

The guidelines noted that a lack of consistency on key

assumptions affected the comparability of options. Accord-

ingly, to help ensure consistency and improve comparability,

the following parameters should be considered (73):

• Phased improvement or system implementation scenar-

ios; that is, each tolling point should change in response

to the addition of new tolled segments.

• The definition of the study area should encompassthose transportation facilities that could influence the

candidate toll project. Route classification and lane

configurations of competing facilities should also be

considered.

• Toll diversion assumptions, including general toll

attraction and composite toll attraction. Composite toll

attraction includes ramp-up, electronic toll collection,

toll rate adjustments, and toll utilization.

• Toll transactions estimates are required for all four of

the study levels described previously.

• Traffic growth constraints should be considered, owing

to the long time frame (40 years) of the forecast. Con-

straints could include highway corridor capacity, com-peting toll facilities, economic development, etc.

• Trip rate equivalence or toll equity, which considers

the average toll rate paid by the user traveling all

possible origin–destination paths on the facility. The

toll rates should fall within an acceptable toll rate

ratio.

The TTA has prepared a series of brochures related to

tolling, including a “Toll Feasibility Analysis Guide,” which

summarized the four levels of analytical studies and their

main characteristics (74).



38

Another TTA document noted that the major bond rating

agencies looked for the following topics to be addressed as

part of toll road demand and revenue forecasts (2):

• Land use and demographic assumptions, including pop-

ulation and employment information;

• Highway network and alternative routes that are bothfeeding the project or competing with the project;

• Weekday versus weekend traffic;

• Review of travel demand parameter assumptions;

• Trip-making characteristics;

• Truck percentage and generated revenue, because of the

impact trucks can have on toll revenue;

• Peak-period versus off-peak management, especially in

managed lane or congestion pricing projects;

• Value of time;

• Ramp-up period;

• Violation rate;

• Toll rates and increases;

• Point estimate forecasts; and• Economic and political risk.

TRAFFIC RISK INDEX

One financial analyst developed a Traffic Risk Index to

assess and compare the risk associated with a given traffic

and revenue forecast according to 10 facility attributes.

Most of the attributes are divided into subattributes. A

notional scale of from 0 to 10 assessed the risk for each

attribute, with higher values reflecting increasing inherent

uncertainty. Descriptions are provided for each extreme to

help illustrate the range of risk that would be consideredfor the particular subattribute. For example, the fourth

attribute, “forecast horizon,” refers to near-term forecasts

as having the least degree of uncertainty and long-term

(“30+ year”) forecasts as having the greatest uncertainty.

The Index was described as a “starting point for consider-

ing toll-project traffic uncertainty in a logical and consis-

tent manner. The Index also represents a checklist that can

be used to examine project-specific uncertainties andprompt appropriate enquiries (allowing the analyst to draw

his or her own conclusions about the likely reliability of

the resultant forecasts)” (9). The means of assessing the

risk are determined by the user, who also can tabulate or

weight the results at his or her discretion. The Index is

reproduced in Table 3.

SUMMARY OBSERVATIONS

Four observations are useful in summarizing the checklists,

guidelines, and indexes.

• Each attempts to introduce consistency and a system-

atic approach to developing traffic and revenue fore-

casts.

• However, neither method nor the application of the

lists, guidelines, or indexes is specified or prescribed;

rather, these are left to the discretion of the user.

• A large range of attributes is described.

• The types of attributes that are considered varies

according to the perspective: The user (i.e., financial)

community’s lists and indexes generally describe the

conditions and environment within which the traffic

model and forecasts are developed, as well as on the

assumptions and inputs, whereas the modeling commu-nity focuses on the model specification methods.



39

ProjectAttribute

0 1 2 3 4 5 6 7 8 9 10

Tolling

regime

• Shadow tolls • User-paid tolls

Tolling

culture

• Toll roads well established-—data on

actual use are available

• No toll roads in the country,

uncertainty over toll acceptance

Tariff

escalation

• Flexible rate setting/escalationformula; no government approval

• All tariff hikes require regulatoryapproval

Forecast

horizon

• Near-term forecasts requirement • Long-term (30+ year) forecasts

Toll facility

details

• Facility already open • Facility at the very earliest stages of planning

• Estuarial crossings • Dense, urban networks

• Radial corridors into urban areas • Ring roads/beltways around urbanareas

• Extension of existing road • Greenfield site

• Alignment: strong rationale (including.tolling points and intersections)

• Confused/unclear road objectives (notwhere people want to go)

• Alignment: strong economics • Alignment: strong politics

• Clear understanding of future highway

network

• Many options for network extensions

exist

• Stand-alone (single) facility • Reliance on other, proposed highwayimprovements

• Highly congested corridor • Limited/no congestion

• Few competing roads • Many alternative routes

• Clear competitive advantage • Weak competitive advantage

• Only highway competition • Multi-modal competition

• Good, high-capacity connectors • Hurry up and wait

Surveys/

data

collection

• “Active” competition protection (e.g.,traffic calming, truck bans)

• Autonomous authorities can do whatthey want

• Easy to collect (laws exist) • Difficult/dangerous to collect

• Experienced surveyors • No culture of data collection

• Up to date • Historical information

• Locally calibrated parameters • Parameters imported from elsewhere(another country?)

• Existing zone framework (widelyused)

• Develop zone framework from scratch

Users:

private

• Clear market segment(s) • Unclear market segments

• Few key origins and destinations • Multiple origins and destinations

• Dominated by single-journey purpose(e.g., commute, airport)

• Multiple-journey purposes

• High-income, time-sensitive market • Average/low-income market

• Tolls in line with existing facilities • Tolls higher than the nor m extendedramp-up?

• Simple toll structure • Complex toll structure (local

discounts, frequent users, variablepricing, etc.)

• Flat demand profile (time-of-day, day-of-week, etc.)

• Highly seasonal and/or peak demandprofile

Users:

commercial

• Fleet operator pays toll • Owner–driver pays toll

• Clear time and operating cost savings • Unclear competitive advantage

• Simple route choice decision making • Complicated route choice decision

making

• Strong compliance with weight

restrictions

• Overloading of trucks is commonplace

Micro-

economics

• Strong, stable, diversified local

economy

• Weak/transitioning local/national

economy

• Strict land use planning regime • Weak planning controls/enforcement

• Stable, predictable population growth • Population forecast dependent on

many exogenous factors

Traffic

growth

• Driven by/correlated with existing,

established, and predictable factors

• Reliance on future factors, new

developments, structural changes, etc.

• High car ownership • Low/growing car ownership

Source: Bain and Wilkins (9).

TABLE 3TRAFFIC RISK INDEX



This chapter presents several conclusions and observations

derived from the study. The chapter closes with suggestions

for further methodological and procedural research on toll

road demand and revenue forecasting taken from the litera-

ture and the survey of practitioners.

The survey of practice and the literature corroborated and

detailed several issues of concern, most of which had been

identified in the scope for this synthesis. Several conclusions

were reached.

• Many of the problems that had been identified with the

performance of traffic and revenue forecasts were

related to the applications of the models, less so to

methods and algorithms. In particular, assumptions

regarding land use, network inputs, values of time, and

other inputs; the process of reviewing models and their

results; and the treatment of uncertainties and risks

were most often cited in the literature in explaining why

the performance problems occurred.

It is noteworthy that much of the literature that

describes these problems in the context of toll road

demand and revenue forecasts comes from the financialcommunity, rather than the transportation modeling

community. This suggests a disconnect between the two

communities; the latter being the “traditional” users (and

developers) of the models and the former representing

the new users. The financial community’s concerns also

parallel those of other new users, such as those involved

in air quality conformity analysis, which suggest that the

state of the practice in travel demand modeling has not

kept pace with the issues that the models now must

address. The disconnect is exemplified in different ways;

for example, in the use of risk analysis (incorporating a

range of outcomes) and stress tests (which assess

extreme and multiple “shocks”); neither of which has

been widely applied to transportation planning. Another

example is the treatment of the impact of short-term eco-

nomic recessions on long-range demand forecasts, which

is starting to be considered. A third example is the more

explicit understanding of the role of different economic

influences, such as gender, age, and occupation, on toll

road choice.

It is also incumbent upon the new users to under-

stand how the models work and how to interpret their

results, as well as their inherent limitations; and it is

equally incumbent on the developers of the models and

40

their data to provide this understanding. One traffic

and revenue study reviewed for this synthesis noted

that “professional practices and procedures were used

in the development of the traffic and revenue forecasts

included in this report” as a preface to its overview of

the modeling process, which was brief and did not pro-

vide many details. Doubtless this is true; however, the

ensuing documentation provided very little informa-

tion that could help analysts understand the input or

modeling assumptions, let alone address the questionsposed in the preceding chapter’s checklists. On the

other hand, some traffic and revenue studies provided

considerably more explanation of the modeling proce-

dures and, especially, of the input assumptions and

how they were derived.

• Nonetheless, improvements in both aspects (application

and method) are required to address the performance of

the models in traffic and revenue forecasts.

• Although the application of state-of-the-art method-

ological improvements into common practice—such as

activity-based models and network micro-simulation—

should be expected to improve the state of the practice,

it is likely that these alone will not improve the perfor-mance of traffic and revenue forecasts.

Notwithstanding, several methodological improve-

ments might be made. Two important improvements to

the travel demand modeling process are time-of-day

choice modeling and the modeling of commercial and

truck traffic. The understanding of traveler behavior

when faced with tolls continues to evolve and must be

better understood; that is, a more comprehensive under-

standing of the actual determinants of choice are re-

quired, including values of time and willingness to pay

when confronted with different factors (such as the toll

collection method). The explicit incorporation of risk

and uncertainty in all aspects of the modeling process

also is needed, as is consideration of inputs and outputs

in terms of ranges rather than as absolutes.

It could be argued that each of these areas requires

further research and development before it can be

implemented properly (e.g., the literature indicates that

time-of-day choice modeling is an emerging topic).

Conversely, it also could be argued that simplified (or

better) methods already exist for accounting for each

topic and would represent an immediate improvement

to the forecast if considered (assuming, of course, that

assumptions and methods are treated transparently).

CHAPTER FIVE

CONCLUSIONS



41

Finally, more generally, a basic prerequisite to any toll

demand and revenue forecast (and to the ability to

account for any of the previously cited improvements)

must be the ability to model the four components of

travel (generation, distribution, mode choice, and route

choice) as a starting point.

• According to the literature, questions regarding the per-formance of the models and forecasts have been posed

mainly by the financial community, rather than by the

transportation modeling community (with some notable

exceptions). The latter community has focused on

improving the methodological basis of modeling and its

underlying data, as demonstrated by the extensive litera-

ture and research on the topic. However, as suggested in

the previous point, this emphasis has not generally recti-

fied the performance problems. In other words, this

suggests that there is a disconnect between the develop-

ers of the models and their users, with the latter having

evolved from the traditional decision makers to those

with different or more rigorous decision-making criteria.• In turn, this suggests that those in the transportation

community who are making investment decisions

regarding tolled facilities do not always know which

questions to ask of their modeling and forecasting

efforts—in other words, the analytical and modeling

capabilities available to them have not always kept

pace with the needs. This was demonstrated by the

large number of explanations that were cited in the

literature and in the survey as causes of performance

failures. In some cases, the explanations were contra-

dictory; in particular, the financial community cited

application as a key problem, whereas survey respon-

dents cited model method as the problem. This can beexplained in part by the relative newness of toll roads

in some parts of the country and the corresponding lack

of a long-term performance history. It also can be

explained by the changing nature of the tolled facili-

ties, in which parts of a facility (i.e., individual lanes)

are now being tolled: this changes the analytical

requirements significantly. The problem is exacerbated

by the “confidential” or “proprietary” nature of the

forecasts and methods that are developed for toll roads,

and also by “optimism bias” on the part of the sponsor,

local elected officials, or other advocates of the pro-

posed toll road.

• Observers have remarked, informally, that there is no

standardization in the toll road demand and revenue

modeling and forecasting processes. Also, they have

questioned whether such standardization could be pro-

moted in the community. Given that this mirrors a sim-

ilar lack of standardization in travel demand modeling in

general, as noted, that the state of the art in modeling

continues to evolve, and that there are several different

and valid techniques (e.g., as in toll diversion modeling),

it may be more appropriate instead to do two things.

First, standardize the terminology or at least list and cat-

egorize the different definitions for key terms; and sec-

ond, develop a commonly used set of questions and

attributes that should be considered. As an example of

the first, there is no single, commonly understood defini-

tion of what is meant by an “investment grade” traffic

and revenue forecast: it may be more appropriate instead

to develop a list of how different organizations under-

stand, interpret and use this term. An example of the

second is the Texas Turnpike Authority’s guidance (see

chapter four), which provides a practical treatment that

perhaps could be reviewed for general application

across the United States, taking into account the differ-

ent perspectives of the owners, sponsors, and financial

community. The Traffic Risk Index similarly provides

a useful starting point for elaborating on the specific

questions that should be asked (more precisely, the vari-

ables that should be taken into account and their

impacts) in the development of toll road demand and

revenue forecasts.

In their own right, standardizing and understanding

the terminology will not improve the process and

results of traffic and revenue forecasts. However, these

improvements will encourage practitioners and owners

to understand more clearly the objectives and require-

ments of decision makers and financial sponsors, and

ensure in turn that the components of the forecasting

process are more responsive to these needs.

• Several observers have noted that “you get what you

pay for.” In other words, in the United States, sufficient

resources have not been devoted to procuring the

required data or to updating older data, or to calibrating

models to the level of detail that is required. The gen-

eral practice in Europe, for example, is to prepare three

sets of forecasts. This clearly requires an investment on

someone’s part. Intuitively, better data, more detailed

models, and multiple forecasts should improve model

performance. Similarly, it is intuitive that a stronger

role for peer review should improve the performance;

however, the literature review did not uncover any eval-

uations or specific assessments of the effectiveness of

the peer review process for modeling in general.

• It should also be noted that the comparisons in the liter-

ature focused primarily on the revenues as opposed to the

demand (i.e., the traffic and its composition). The litera-

ture noted that revenue performance could be affected by

changes in toll rates or by drawing on reserves, or other

means; in other words, by actions that are not related

directly to demand. Therefore, at least some of the avail-

able information does not accurately reflect the outputs

of the travel demand model and, accordingly, the linkage

between the demand forecasts and the revenue perfor-

mance is not always completely direct or explicit.

Accordingly, there is a need to measure the performance

of the travel demand models in their own right, specifi-

cally examining how well the toll road demand models

simulate each class of vehicle and traveler.

The research literature cited in this synthesis largely

focused on methodological issues; notably, the understanding



of the variables that affect the traveler’s decision to use a toll

road, consideration of probability distributions to describe

these variables as a means of analyzing and managing risk,

development of time-of-day choice models, simulation of

value of time and the role of stated preference surveys in esti-

mating value of time or on the development of value-of-time

models based on historical data that are now becoming avail-able. The need for continued research in these areas was

inherent in the literature, with specific recommendations for

research comprising the following.

The financial community and practitioners made several

explicit recommendations for further research.

• Improve understanding of the impact of electronic toll

collection (more generally, the type of tolling collec-

tion) on value of time.

• Incorporate risk analysis into the demand forecasting

process, accounting for multiple possible inputs and

outputs.• Account for the impacts of changed economic condi-

tions (notably, recessions and lower than expected

initial demographic and economic growth).

• Improve the understanding of and the methods for esti-

mating value of time and ramp-up, including the impact

of existing congestion and expected development on

travel demand and on toll road demand specifically.

• Improve the treatment of trucks and commercial vehi-

cles in toll road modeling.

• Improve the validation of the travel demand models.

• Develop a more improved understanding of the factors

and assumptions that are used to develop the demand

models and of the criteria that are used to assess theirperformance and calibration.

A 2005 review of the practice of modeling road pricing by

Spears made the following five recommendations for research.

1. Document case studies of transportation planning

agencies that have incorporated road pricing in their

42

travel models to provide details “concerning changes

in model structure, data requirements, value-of-time

parameters, calibration and validation considerations,

and specific application results from other modeling

efforts.”

2. Compile and synthesize current and past empirical

research on value of time and value of reliability, thencompile the existing research on value of time into “an

application-oriented document that provides travel

modelers with reasonable ranges for [value of time],

classified by income level, trip purpose, or other rele-

vant parameters,” and includes practical guidance on

value-of-time adjustments (such as the impact of elec-

tronic toll collection). A similar compilation of the

more limited research on value of reliability was also

recommended, as well as the identification of priority

areas for additional research and data.

3. Encourage data collection on travel behavior on road

pricing projects. Funding for such projects should

account for additional empirical data collection(notably, on value of time and value of reliability), as

well as for an independent evaluation of the project.

4. Conduct basic and applied research to incorporate

time-of-day and peak spreading models in current

travel demand models to address the “principal limita-

tion” in current travel demand models; that is, “the

way in which they distribute daily trips by time-of-

day.” New methods or models are needed to allow for

more precise resolution of daily trip distributions (by

the hour or half-hour), perform efficient multiclass

assignments over multiple time periods, and allow for

a systematic shift of trips between adjacent time peri-

ods to reflect peak spreading.

5. Conduct basic research to better understand and measure

the influence of traffic congestion (and the underlying

factors that influence congestion on a day-to-day basis)

on travel time variability. Archived operational traffic

data exist to explore this relationship, although they

have been largely unused for this subject.



43

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49

All-or-nothing assignment—assignment approach in which

all travel demand for each origin–destination pair is

assigned to the shortest time path based on uncongestedtravel times.

Barrier toll road—has one or more toll plazas that form a bar-

rier across the roadway to prevent free passage. Motorists

are required to pay a toll to continue, and the system usu-

ally includes large mainline toll plazas and small toll

plazas at on and off ramps.

Closed barrier toll—a system that does not allow motorists

to enter the roadway network without first paying a fee.

Generally a payment or a ticket is issued at the entry and

exit points in the roadway network.

Electronic toll and traffic management (ETTM)—the use of

two-way electronic communications between moving

vehicles and roadside sensors for the purposes of toll col-lection and other traffic management functions.

Electronic toll collection (ETC)—the use of automatic elec-

tronic vehicle identification, such as transponders, for non-

stop toll collection.

Equilibrium assignment—a trip assignment algorithm that

takes into account the build-up of volume and the ensu-

ing changes in trip time when allocating trips to links.

From the traveler’s perspective, the assignment simu-

lates the driver’s desire to minimize overall his or her

travel time (i.e., his or her travel costs) between a partic-

ular origin and destination by switching paths. Equilib-

rium occurs when the travel time on all possible paths for

a given origin–destination is equal—that is, the driver no

longer can improve his or her overall trip time by switch-

ing paths. Equilibrium is achieved, usually after several

iterations, when the difference in overall travel time (i.e.,

in generalized cost) summed over all trips and all origin–

destination pairs is very small, between the current and

previous iterations (i.e., is within specified absolute

and/or percentage differences).

Feed-back loops—part of the iterative modeling process

where at the completion of the travel demand assign-

ment, the resulting travel times or costs are fed back into

trip generation, trip distribution, or mode split steps. The

next iteration takes into account these travel costs foreach origin–destination pair and recalculates the travel

demand.

Goodness of fit measures—used to validate models. Com-

monly used measures include the following:

Absolute comparison of the absolute difference between

modeled flow and observed flow, on a given link or across

a screenline.

Relative comparison of difference between the modeled

flow and the observed flow. That is, modeled flow divided

by observed flow, with the comparison typically expressed

as a percent.

GEH statistic combines the absolute and relative com-

parisons. It accounts for two types of error: that associ-

ated with the simulated flow (reflecting the fit of themodel) and that associated with the observed flow

(through data collection errors and the day-to-day vari-

ability of traffic).

The statistic is defined as:

The statistic is defined as:

The GEH is properly a goodness of fit measure, rather

than a statistical test. It is intended to give a quick indi-

cation of how well the model is working. It can, and nor-

mally is, applied to all model links for which observed

counts are available. R2 is the correlation coefficient of the best-fit regression

line of a plot of the modeled flows versus observed

flows. This measure is commonly used to compare flows

across a number of links, whether or not they are part of

the same screenline or route (for example, all arterials in

an urban network for which traffic counts are available).

Correlation coefficients closer to 1.0 indicate a better

goodness of fit.

Open barrier toll—a system that allows motorists to enter the

roadway network at certain locations without immediate

payment prevents the motorist from proceeding past cer-

tain points, unless a fee is paid. It can use electronic toll

collection, which offers drivers the ability to pay tolls

without stopping.

Open road tolling—a system with no physical toll booths. All

tolling is conducted by the identification of vehicles pass-

ing under a gantry at each individual tolling point. Tolls

are collected by one of two methods including electronic

toll collection or video license plate identification system.

Shadow toll—a payment and revenue process where instead

of charging the user directly, businesses, land holders, or

government agencies reimburse the private investor by

accounting for the number and type of vehicles using the

road, therefore allowing the private sector to invest the

capital and the beneficiaries of the toll road to pay per theusage of the facility by the public.

Stress test—used to assess the financial stability of a

project (or of a portfolio of projects). The process reval-

ues the project’s financial performance according to a

different set of assumptions in the face; for example,

of unforeseen “shocks.” Most asset markets lack a his-

tory of returns that provide sufficient information about

the behavior of markets under extreme events: stress

tests complement traditional financial forecasting mod-

els by testing how the project’s value changes in

response to “exceptional but plausible” changes in the

GEHobserved flow simulated flow

obs=

−

×

( )

. (

2

0 5 eerved flow simulated flow− )

GLOSSARY



underlying risk factors. In addition to testing “market

risk” [such as toll revenues less than projected], the

process also examines credit risk (losses from borrower

defaults) and liquidity risks (illiquidity of assets and

depositor runs). Several techniques have been developed

in recent years.

50

Violation rate—a measure of the vehicles that are illegally

evading the tolls compared with the number of vehicles

using the tolled facility. Examples of toll evasion include, but

are not limited to, the following: screening of license plates,

using HOT lanes without the proper number of people, or

using improper electronic pass with an unregistered vehicle.



51

OVERVIEW OF METHOD

A survey was conducted regarding the practice of toll facil-

ity demand revenue estimates. This survey of practitioners

was sent to four specific agency types: state departments of

transportation (DOTs), toll authorities, bond rating agencies,

and bond insurance agencies. The first two types represent

the traditional developers and users of traffic forecasts. The

inclusion of the financial community (the latter two types)

was intended to capture the viewpoints and experiences of

the forecasting and modeling process from as many partici-

pants involved in the process as possible.

Participants were given the option of answering directlyonline through a web-based survey program (Websurveyor),

or completing a hardcopy of the survey, which was included

in the e-mail as a pdf (in which the survey form could be

returned to the consultant by mail or fax).

SURVEY OF PRACTICE

Survey Development

The survey was conducted over a two-month period in April

and May 2005. Before the survey was distributed, it was nec-

essary to establish a comprehensive outline and to test the

survey to ensure that it was as accurate and target-specific aspossible.

During the initial stages of the survey design, it was deter-

mined that the survey should be completed by several differ-

ent types of agencies that are involved in the toll road

demand and revenue estimating process. A logical way to

construct the survey was to structure the questions as close-

ended as possible, to provide a clear indication of what was

being asked and to help in the summary process. Therefore,

most of the questions were asked with a selection or list of

answers and the respondent was instructed to select a single

answer, or as many answers as applied, depending on the

question. However, given the nature of the subject, a stan-

dard set of answers likely would not always have covered all

possibilities. Therefore, for every question there was also the

option of selecting “Other,” with space provided to allow

expansion of the respondent’s answer.

The research team developed a survey focusing on state-

of-the-practice applications and techniques. Specifically,

important information that was targeted for acquisition was:

• Who is the respondent to the survey?

• What types of modeling processes are being used?

• What variables are being modeled?• How are the models being calibrated and validated?

• Are there specific case studies or examples?

• What is the respondent’s experience with the model

forecasts?

The survey was divided into three self-contained sections

(designated as Parts I, II, and III). This made the survey more

“respondent friendly,” to specifically target areas of interest

in the modeling and forecasting processes.

Part I determined the type of agency that was responding

to the survey and who from that agency was responding. It

also requested a discussion of their philosophy, in terms of their use of tolling technologies, what type of facilities was

tolled, etc.

Part II pertained to the forecasting model itself and its

variables. This section was answered either by the original

respondent, if the responding organization performed the

modeling and forecasting in-house, or it could be forwarded

to a consultant or the agency that actually developed or

applied the model for the original responding agency. This

section asked the respondent to describe in detail the type of

model used and the parameters of the model in terms of

inputs, structure, modeled trip purpose, calibration tech-

niques, validation checks, etc.

Part III asked the respondent to discuss a specific exam-

ple of a toll road traffic demand and revenue study carried out

by the responding organization. Again, if the original respon-

dent did not do the actual analysis, the survey could be

passed to the consultant or outside agency responsible for the

study. The purpose of the final section was to determine the

results of the previously described model (Part II) and

whether there were major or minor problems with the analy-

sis, whether they were identified, how and if they were cor-

rected, etc.

A pilot survey session with one respondent was

conducted to assess the usability and readability of the

survey before distribution to all participants. The pilot

participant remotely completed the survey while speak-

ing his/her thoughts out loud over the telephone to the

research team. Issues were identified related to the clarity

of the survey and its ability to extract the desired informa-

tion. Minor modifications were made to the survey fol-

lowing the pilot survey session. Subsequently, the survey

was sent to state DOTs, tolling authorities, bond rating

agencies, and bond insurance agencies throughout the

United States.

APPENDIX A

Development and Administration of Survey



Contact names and e-mail addresses of the DOTs were

provided by TRB, for the toll authorities by the International

Bridge, Tunnel, and Tolling Association (IBTTA), and for

the bond rating agencies and bond insurance agencies

through the Topic Panel and other individuals. In accor-

dance with the scope of the synthesis, the survey was

directed to respondents in the United States, although it wasalso sent to three Canadian provincial/territorial ministries

of transportation and one Canadian tolling agency that had

been included in the IBTTA list. Two follow-up reminders

were sent at regular intervals to those who had not yet

responded. Two methods of completing the survey were

provided for the practitioner. The first was to complete the

survey online through a commercial online survey host

(Websurveyor). The second option was to allow the practi-

tioner to complete the survey by hand on a hard copy. To

this end, a pdf version was included in the initial e-mail. The

survey could be returned by mail or fax. Support was pro-

vided throughout the process by means of e-mail correspon-

dence, as well as a toll-free telephone number distributedwith the survey.

Upon review of the survey responses, follow-up tele-

phone interviews were initiated with respondents where

additional information was required or to clarify unclear

or contradictory responses. Moreover, if the survey indi-

cated an exceptional toll modeling practice, then further

details were sought to document the techniques and

methodology.

Discussion of Survey Response

Two unexpected problems were encountered. These consti-

tute important findings in their own right; however, they

also affected the results of the survey and the subsequent

analysis.

Several respondents indicated that they used relatively

straightforward spreadsheet programs to generate forecasts.

It should be observed that these practices of modeling were

used to forecast short-term travel demand and revenue for

52

existing toll facilities that have been in operation for many

years. Simple modeling techniques were designed to forecast

short-term annual demand and revenue for operations and

maintenance, based largely as an extrapolation of previous

years’ experience, or by using elasticity factors.

There was also an issue pertaining to the compatibility of Parts II and III of the survey. In Part II, respondents were asked

to describe the application of their model and the data to a

specific demand and revenue forecast of their choice. This

focus on a single model, application, and forecast was intended

to avoid confusion among those respondents that had several

models, applications, or forecasts. Most respondents selected

the newest model and its use in a recent toll road feasibility

study, generally ensuring that only the latest modeling tech-

niques (the state of the practice) were described. However, in

Part III, the respondents were asked for their experiences with

actual toll road demand and revenue; specifically, how it

related to the model described in Part II. Because many

respondents had chosen their newest project for Part II, theywere unable to provide corresponding state-of-the-practice

responses to Part III.

This raised an interesting point in general, namely that the

state of the practice in toll road demand and revenue fore-

casting is difficult to capture definitively, because the latest

methods are still unproven or at least there are few actual per-

formance data to substantiate whether the new modeling

techniques are more or less successful than previous tech-

niques. Because of the required lengthy lead time before the

completion of the design and construction of toll roads, the

only models with enough data to be able to determine their

successes and failures are 10 or more years old. Accordingly,a caveat must be placed on what actually constitutes the state

of the practice insofar as the results of the modeling process

itself are concerned, because some of the new techniques are

still in their infancy and the old techniques are being changed

and updated.

Of the 138 surveys that were sent, there were 55 respon-

dents, for a response rate of 40%. The corresponding rates

of return by agency are found in Table A1. Of the 55 surveys

Agencies

No. of

Surveys Sent

No. of Surveys

Returned

Rate of Return

(%)

State DOTs 56 29 52

Tolling authorities 70 21 30

Canadian authorities 4 3 75

Bond rating agencies 3 2 67

Bond insurance agencies 5 0 0

Total 138 55 40

TABLE A1RATE OF RETURN BY AGENCY



53

returned, 10 were e-mail responses indicating that the state

did not own or operate, or was not planning to own or oper-

ate, a toll road.

Question I.1 of the survey asked respondents to indicate

their interest or mandate in toll road facilities by selecting

one of the following five options:

• We are currently or plan to be an owner/operator

• Bond rater/insurer

• Travel forecasting or other consultant/independent

reviewer

• None planned or expected

• Other.

Of the remaining 45 survey respondents, 19 indicated that

they were neither planning nor expecting to own or operate

a toll road, and therefore did not complete the remainder of

the survey. Of the remaining 26 respondents, three selected

“other” (see Appendix B for detailed responses) and one of

those respondents indicated that he/she could not answer the

survey.

In summary, there were a total of 25 completed responses

to the survey. Of these, 23 completed Part I (two bond rating

agencies that responded were directed to skip Part I, and alsoskipped Part II), 13 respondents completed Part II, and

13 respondents completed Part III. Tables A2 and A3 sum-

marize in more detail which types of agencies completed

Part II and Part III, respectively.

Of the total number of respondents, only 23 (42%) indi-

cated that they were currently or planning to be an owner or

operator of a toll road facility. Table A4 shows the percent-

age for all respondents.

A complete summary of the survey results is provided in

Appendix C.

Agencies

No. of Respondents That

Currently or Plan to

Be an Owner/Operator

No. of Survey

Respondents

Percentage

State DOTs 7 29 24

Tolling authorities 15 21 71

Canadian authorities 1 3 33

Bond rating agencies 0 2 0

Bond insurance agencies 0 0 0

Agencies Completed Part II

State DOTs 3

Tolling authorities 9

Canadian authorities 1

Bond rating agencies 0

Bond insurance agencies 0

Total 13

Agencies Competed Part III

State DOTs 2

Tolling authorities 8

Canadian authorities 1

Bond rating agencies 2

Bond insurance agencies 0

Total 13

TABLE A2RESPONDENTS COMPLETING PART II

TABLE A3RESPONDENTS COMPLETING PART III

TABLE A4SUMMARY OF RESPONSES TO QUESTION I.1 OF SURVEY



54

APPENDIX BSurvey Questionnaire

TRANSPORTATION RESEARCH BOARDof the National Academies

April 7, 2005

The Transportation Research Board (TRB) is preparing a Synthesis on “Estimating Toll RoadDemand and Revenue.” This is being done for the National Cooperative Highway Research Program,under the sponsorship of the American Association of State Highway and Transportation Officials incooperation with the Federal Highway Administration. The objective of this Synthesis is to provide

an overview of state DOT and other highway facility owner’s practices, recent literature findings, andresearch-in-progress addressing the subject topic.

David Kriger of iTRANS Consulting Ltd. is preparing this synthesis report under contract to TRB.To assist Mr. Kriger, we request that you complete the attached survey or forward it to the most

appropriate person(s) in your agency. The report will serve as a resource to all states and has thepotential to improve methods and technologies for estimating demand and revenues for planned tollroads. This is a very timely topic, and your agency’s response to the survey is extremely important tothis study. Please note that, while lengthy, the questionnaire is mostly multiple choice.

Please have the completed questionnaire and any supporting materials returned to

Mr. Kriger by April 22, 2005.

The questionnaire has been provided in two formats:

1) As a web-based questionnaire that can be filled out and returned on-line, in multiple sessions if needed.2) As a PDF file that can be printed, filled out, and faxed or mailed (this information can be found at

the end of the survey).

If additional time or information is needed to complete the questionnaire, please contact Mr. Kriger at888-860-1116, or me at 202-334-3245. Thank you for your assistance.

Jon WilliamsManager, Synthesis StudiesTransportation Research Board500 Fifth Street, NWWashington, DC 20001phone: 202-334-3245fax: 202-334-2081email: [email protected]



55

Estimating Toll Road Demand and Revenue

Thank you for participating in our survey on the state of the practice in estimating toll roaddemand and revenue. Please complete survey by April 22, 2005.

The survey is divided into three parts:

• Part I asks you to provide some background information regarding your organization’sinterest and/or mandate in toll road demand forecasting.

• Part II asks you about your organization’s travel demand forecasting models for toll roadtraffic and revenue forecasts and about the data and assumptions that support the modelsand forecasts.

• Part III asks how the model outputs have been used for toll road studies and financing,and about your experiences with the performance, accuracy, and effectiveness of thetolling forecasts.

• Parts IV and V are identical to Parts II and III, respectively. They are provided shouldyou wish to describe more than one specific model application.

Important note: For cases in which a consultant or someone outside your organization preparedthe model or forecasts, please complete Part I (background information) before asking theconsultant or outside organization to complete Parts II and III.

Documentation. To support our research, we seek copies of relevant reports: For example, reports

and documents that describe your travel demand forecasting model, its calibration, the underlying dataupon which the model is based, the tolling forecasts for which it has been used, reviews and audits of themodel and/or forecasts, and any other information that you see as being relevant.

Contact Information

The following information is needed to help us ensure that our survey has covered a broad rangeof perspectives. We also might need to follow-up with you for clarifications of your responses.Please be assured that your contact information will be kept strictly confidential and will not bedisclosed to outside parties.

Name:

Title/Position:

E-mail:

Telephone Number:

Organization:

Department/Group:

Location (City/State):



56

Part I. Background information

I.1 What is your organization’s interest and/or mandate in toll road facilities? Please check (✓) one box only.

We are currently or plan to be an owner/operator (→ please go to Question I.2

below)

Bond rater/insurer (→ please go to Section II )

Travel forecasting or other consultant/independent reviewer (→ please go to Section

II )

None planned or expected (→ please go to the end of the form and submit your

survey)

Other ( please explain):

I.2 What types of tolled facilities does your organization currently or plan to own or operate?Please check (✓) all boxes that apply.

Current Planned

Facility Type Own Operate Own Operate

Urban expressway

Rural or intercity expressway

HOT lane

Bridge or tunnel

Urban arterials

Rural arterial highways


I.3 What tolling technologies are used or are planned? Please check (✓) all boxes that apply.

Tolling Technologies

CurrentlyUsed Planned

Traditional toll plaza (manual toll collection only)

Toll plaza with combination of manual and electronic tollcollection (EZPass, FasTrak, etc.)

Fully electronic toll collection (transponder, record licenseplate)




57

Source of Financing


Federal government (all sources)State government

Local/county/district government

Public–private partnership

Private sector

Bond financing


I.5 For what purpose(s) are the generated revenues used or planned? Please check (✓) allboxes that apply.

Use of Generated Revenues


Facility construction

Facility operation and maintenance

Expansion of existing facility

Other roads or highways

Public transit

Debt service on bonds

General tax revenues (not specific to the facility)


I.6 What are the current or planned toll structure and rates for your toll roads?

Toll Structures


Point toll (e.g., expressed as $/trip)

Distance toll—fixed (e.g., expressed as $/mile)

Distance toll—variable (e.g., expressed as $/mile)


I.4 What sources of financing were used or are planned for the construction of these toll

road facilities? Please check (✓) all boxes that apply.



58

Conducts peer reviews or critical reviews Uses the travel demand forecasts to prepare revenue forecasts or conduct a financial

analysis

Uses the travel demand forecasts to approve or ensure funding

Other ( please describe):

I.8 If your organization does not prepare its own toll road demand forecasts internally, whoprovides the forecasts for your use? Please check (✓) all boxes that apply.

Consultant (→→→→ Please have your consultant complete Part II and Part III.)

Regional MPO/COG

State DOT

Other ( please identify):

Part II. State of practice in travel demand forecasting models for toll

road demand and revenue forecasts

The questions in Part II ask you to describe the application of your models and data to a specific demand and revenue forecast or study of your choice.

Examples of the scope of applications are: system-wide feasibility or policy studies, forecasts for

new facilities, forecasts for extensions or widenings of existing facilities, forecasts for HOT lanes,

etc. The applications could have been used for concept studies, feasibility studies, policy studies,

investment grade forecasts, design forecasts, critical reviews or audits, etc.

Some organizations have developed models or forecasts for more than one application. If so,

please use the most recent/up-to-date application when answering the questions below. If, for

any reason, the most recent application is not a good example of the state of the practice in your

organization, please pick a more appropriate example and briefly explain your selection.

II.1 Please describe the specific application for which the model was (or is to be) used.

Facility or study name:

Date of study or forecast:

I.7 What is your organization’s role in or use of toll road demand and revenue forecasts?Please check (✓) all boxes that apply.

Prepares travel demand (traffic) models/forecasts

Prepares or collects the data that are used for the travel demand models/forecasts



59

Location—state/county/city/town:

Description of facility (facilities)—urban/rural, type, length, number of lanes, cross section (e.g., ruralexpressway, 35 miles, 6 lanesdivided):

Toll structure (point or distance toll,variable or fixed, breakdown of rates):

II.2 To what type of analysis was the model applied? Please check (✓) all boxes that apply.

Conceptual plan/feasibility study

Policy study

Alternatives analysis

Investment grade forecast

Design forecast

Critical review or audit

Risk assessment analysis


II.3 Please describe the following basic characteristics of your travel demand forecastingmodel. Please answer all questions and/or check (✓) all boxes that apply. (Please also

provide any relevant documentation that describes your model.)

− Software used for the model:

− Area covered by model: _____ square miles _____ acres

other ( please define):

− Population covered by model:

− Base year of calibration:

Organization that prepared the studyor forecast:

(Expected) year of facility opening:

Horizon year(s) for which forecasts

were prepared:



II.5 Please describe the types of links (tolled and not tolled) in your model’s networks.

Please check (✓) all boxes that apply.

Not Tolled Tolled

Expressways

Arterial roads or highways

Collector roads

Local roads

Transit network

Other: ___________

Expressways


Collector roads

Local roads

Other: ___________

II.6 Please describe the modes that are modeled. Please check (✓) all boxes that apply.

Single-occupancy passenger vehicles (SOV)

High-occupancy passenger vehicles (HOV)

Vehicles in commercial use (e.g., repair vans, taxis, courier trucks, etc.)

Trucks (light and heavy)

Emergency/military vehicles

Buses (transit, commuter, school, intercity, etc.)

Passenger rail (light rail transit, commuter, intercity, etc.)


II.7 Please describe the time periods modeled. Please check (✓) all boxes that apply.

Weekday AM peak hour/half-hour/period

Weekday PM peak hour/half-hour/period

Weekday other peak hour

Weekday off-peak hour

Weekday 24 hour

Weekend


II.4 Please describe the characteristics of your model’s networks and zone system.

− Number of traffic zones:

− Number of network links:

− Number of network nodes:

60



II.9 Please describe the model structure. Please check (✓) one box only.

Traditional four-step (generation, distribution, modal split, assignment)

Trip assignment only (no demand modeling, or demand is forecast externally to themodel)

Activity-based modeling


II.10 Does the model have feedback loops? Please check (✓) one box only.

Yes—assignment impedances are fed back to distribution and/or modal split for____ cycles

Yes—other ( please describe):

No feedback loops

II.11 Please describe the trip purposes modeled. Please check (✓) all boxes that apply.

Work (commute)

Work-related

Out-of-town business trip

School/education

Shopping

Leisure/recreation

Personal business (e.g., medical appointment)

Serve passenger (pick up/drop off)


II.12 Please describe the formulation of the modal split model (methods for mode choicemodeling). Please check (✓) all boxes that apply.

Logit or similar

II.8 Please indicate how the peak hour is derived. Please check (✓) one box only.

Modeled directly in demand model (no factors)

Factors or percentages applied to trip tables before assignment


61



62

All-or-nothing

Capacity restraint


II.14 Please describe the methods used for time choice modeling. Please check (✓) all boxes

that apply.

Peak spreading model

Time choice model

Arrival/departure time choice model

Factors (e.g., peak to 24-hour factors) from surveys, traffic counts, or other sources


No time-of-day choice models

II.15 Please describe the tolling costs that are modeled.

Value of time: [please check (✓) all boxes that apply]

By mode By vehicle class By purpose

Willingness to pay: [please check (✓) all boxes that apply]



Willingness to pay: the value of time that accounts for how much travelers value different attributes of theproposed facility, as opposed to its mere availability. Typically, willingness to pay is greater than the value

of time. Reasons that have been put forward to explain the higher value include the assumed safety andconvenience that the new facilities would provide.

II.16 Are variable tolls modeled? Please check (✓) one box only.

Yes

No

Diversion curve

Factors


No modal split model

II.13 Please describe the formulation of the trip assignment model (methods for route choicemodeling). Please check (✓) one box only.

Equilibrium assignment



63

Model was updated/enhanced from an existing model that was calibrated forprevious toll demand and revenue forecasting studies

Model was updated/enhanced from an existing model that was calibrated for otherpurposes (e.g., LRTP, TIPs, etc.)

Existing model was used as is without special adaptations


Don’t know

II.19 Are there any special or innovative features related to this application, not otherwisedescribed above?

II.20 What data were used to calibrate or validate your model? Please check (✓) all boxes that

apply.

Data Calibration Validation

Origin–destination survey

Activity- or tour-based survey

Stated preference survey

Traffic counts

Speed/travel time surveys

Land use inputs

Network characteristics

Other ( please explain): ____________________________

II.17 How are tolls taken into account in trip assignment (route choice)? Please check (✓) allboxes that apply.

Generalized cost in volume-delay f unction

Diversion curves


II.18 Was this model calibrated specifically for this analysis? Or was it adapted from anothermodel? Please check (✓) all boxes that apply.

New model was calibrated specifically for this toll demand and revenue forecast



64

None/not done (→ please proceed to Part III )

Judgment/reality check (e.g., comparison with older forecasts)

Statistical verification

Risk assessment

Sensitivity tests of key parameters or inputs

Use of ranges of forecasts

Independent audit/critical review


II.23 Were changes, corrections, or improvements recommended for the model or the forecastsas a result of the verification?

Yes ( please explain):

No

II.24 Were these recommendations implemented?

Yes—all

Yes—some ( please identify):

No—not implemented in current model or forecasts Planned for implementation in future model and forecasts

II.21 What tests were used to validate the base year model assignment results? Please check (✓) all boxes that apply.

Comparison of ratios of simulated and observed volumes at screenlines, cutlines,cordons, etc.

Statistical tests (R2, RSME, GEH, etc.) comparing simulated and observed link

volumes

Comparison of simulated and observed travel times or speeds on links, facilities,corridors, etc.


No tests/don’t know

II.22 Please indicate how your model and/or toll road forecasts have been verified. Pleasecheck (✓) all boxes that apply.



65


None

Part III. Experience with toll road demand and revenue forecasts

The questions in Part III refer to the application you described in Part II.

III.1 Please describe how your toll road demand forecasts have compared with actual demandconditions or with the expected performance of your facility.

Ramp-up forecasts versusactual conditions

Traffic forecasts overstated by ______%

Traffic forecasts understated by _______%

Traffic forecasts within 5%

Medium-term (5–10 years)forecasts versus actualconditions (if available to the

application you aredescribing)




Long-term (>10 years)forecasts versus actualconditions (if available to theapplication you aredescribing)




Yes, with qualifications or conditions ( please explain):

No ( please explain):

II.26 Were network micro-simulation models used in the development of demand and revenueforecasts (e.g., for a HOT lane)?

Yes

No

II.27 What other type(s) of models were used for this application? Please check (✓) all boxes

that apply. Land use/economic forecasting model (what software?)

Traffic operations model (what software?)

II.25 Have the results of your forecasts been accepted by the intended audience/decisionmakers?

Yes, unconditionally



66

III.3 What factors influenced the performance of the forecasts identified in Question III.1?Please check (✓) all boxes that apply.

Inputs:

Assumptions regarding land use or future base network configurations onparallel/competing routes or modes

Availability, appropriateness, or sufficiency of data, models, or analytical

capabilities Values used for value of time/willingness to pay and/or other monetary values

Public and political inputs regarding land use and network assumptions

Environmental or economic development considerations

Modeling:

Model structure

Process of expanding modeled time periods to annual forecasts

Transparency/opacity in the modeling and forecasting processes

Calibration process, coverage, and precision

Some modes were not modeled (e.g., trucks) or were not modeled well

“Control” over how the model outputs were used, analyzed, or interpreted

Model was recalibrated/model networks were reconfigured

Demand forecasts were revised

Revenue forecasts were revised Financing schedule was revised

Performance indicators changed/new indicators implemented

Tolling structure or rates were changed

Project costs changed

Staging or timing of project was revised

Project postponed or cancelled

Policies revised/new policies adopted


No impact (forecasts accepted and used as is)

III.2 What impact did the differences (if any) identified in question III.1 have on the forecastsor on the use of the forecasts? Please check (✓) all boxes that apply.



67

Improve transparency in the modeling and forecasting processes (so that decisionmakers are better informed)

Improve methods for travel demand forecasting modeling

Collect better or more data as the basis for model calibration or to monitorconditions

Provide better training for modeling and planning staff

Find better ways to tie modeling process to organizational/facility business orfinancing plan

Conduct more risk assessments in forecasts

Conduct more critical reviews and audits


III.5 What recommendations from previous forecasting applications did this model (for this

specific application) already incorporate? Please check (✓) all boxes that apply.

Improved transparency in the modeling and forecasting processes (so that decisionmakers were better informed)

Improved methods for travel demand forecasting modeling

Collected better or more data as the basis for model calibration or to monitorconditions

Provided better training for modeling and planning staff

Found better ways to tie modeling process to organizational/facility business orfinancing plan

Impact of tolling technology on actual traffic volumes (e.g., unreadable licenseplates)

Violation rate

Changes in policy, mandate, legislation, ownership, political environment, etc.


Additional Comments:

III.4 What recommendations do you have to improve the usability, accuracy, reliability, andcredibility of travel demand models and forecasts for estimating toll revenues and forfinancing? Please check (✓) all boxes that apply.

Operations:

Staging of proposed toll facility (or other facilities)

Actual operations and system reliability (e.g., congestion levels, operatingspeeds, frequency of incidents, etc.)



68



III.7 What factors would prevent you from implementing these improvements? Please check (✓) all boxes that apply

Lack of financial resources

Lack of staff

Lack of time

Don’t know how to go about it

Not in our mandate

Other priorities


None

III.8 Are there other models that were used for other applications that you would like to

describe?

Yes (→ please go to complete Parts IV and V )

No (→ please go to Question III.8)

Are there any other comments that you would like to make, either on topics that have notbeen addressed earlier or to amplify or clarify what you have already said?

Conducted more critical reviews and audits


III.6 Which of these recommendations do you plan to implement in your models or in yournext application? Please check (✓) all boxes that apply.





Find better ways to tie modeling process to organizational/facility business or

financing plan Conduct more risk assessments in forecasts

Conducted more risk assessments in forecasts



69

Thank you!

Please repeat Parts IV and V if you have other models that were used for other applications

that you would like to describe

Part IV. State of practice in travel demand forecasting models for toll


Examples of the scope of applications are: system-wide feasibility or policy studies, forecasts for

new facilities, forecasts for extensions or widenings of existing facilities, forecasts for HOT lanes,

etc. The applications could have been used for concept studies, feasibility studies, policy studies,

investment grade forecasts, design forecasts, critical reviews or audits, etc.

Some organizations have developed models or forecasts for more than one application. If so,

please use the most recent/up-to-date application when answering the questions below. If, for

any reason, the most recent application is not a good example of the state of the practice in your

organization, please pick a more appropriate example and briefly explain your selection.

IV.1 Please describe the specific application for which the model was (or is to be) used.

Facility or study name:

Date of study or forecast:

Organization that prepared the studyor forecast:

Reminder: To support our research, we seek copies of relevant reports: For example, reports

and documents that describe your travel demand forecasting model, its calibration, the underlyingdata upon which the model is based, the tolling forecasts for which it has been used, reviews andaudits of the model and/or forecasts, and any other information that you see as being relevant.

The survey is now complete.

Please submit the survey by fax toll free at 1-888-618-4981 or by mail at the followingaddress:

iTRANS Consulting Inc.100 York Boulevard, Suite 300

Richmond Hill, ON L4B 1J8 Canada



70

Policy study

Alternatives analysis

Investment grade forecast

Design forecast

Critical review or audit

Risk assessment analysis


IV.3 Please describe the following basic characteristics of your travel demand forecastingmodel. Please answer all questions and/or check (✓) all boxes that apply. (Please also

provide any relevant documentation that describes your model.)

− Software used for the model:

− Area covered by model: ___ square miles acres

other ( please define):

−

Population covered by model:− Base year of calibration:

IV.4 Please describe the characteristics of your model’s networks and zone system.

− Number of traffic zones:

(Expected) year of facility opening:

Horizon year(s) for which forecastswere prepared:

Location—state/county/city/town:

Description of facility (facilities)—urban/rural, type, length, number of lanes, cross section (e.g., ruralexpressway, 35 miles, 6 lanesdivided):

Toll structure (point or distance toll,variable or fixed, breakdown of rates):

IV.2 To what type of analysis was the model applied? Please check (✓) all boxes that apply.

Conceptual plan/feasibility study



71

Vehicles in commercial use (e.g., repair vans, taxis, courier trucks, etc.)

Trucks (light and heavy)

Emergency/military vehicles

Buses (transit, commuter, school, intercity, etc.)

Passenger rail (light rail transit, commuter, intercity, etc.)


IV.7 Please describe the time periods modeled. Please check (✓) all boxes that apply.

Weekday AM peak hour/half-hour/period

Weekday PM peak hour/half-hour/period

Weekday other peak hour

Weekday off-peak hour

Weekday 24 hour

Weekend


− Number of network links:

− Number of network nodes:

IV.5 Please describe the types of links (tolled and not tolled) in your model’s networks.

Please check (✓) all boxes that apply.

Not Tolled Tolled

Expressways


Collector roads

Local roads

Transit network

Other: ___________

Expressways


Collector roads

Local roads

Other: ___________

IV.6 Please describe the modes that are modeled. Please check (✓) all boxes that apply.

Single-occupancy passenger vehicles (SOV)

High-occupancy passenger vehicles (HOV)



72

IV.11 Please describe the trip purposes modeled. Please check (✓) all boxes that apply.

Work (commute)

Work-related

Out-of-town business trip

School/education

Shopping

Leisure/recreation

Personal business (e.g., medical appointment)

Serve passenger (pick up/drop off)


IV.12 Please describe the formulation of the modal split model (methods for mode choicemodeling). Please check (✓) all boxes that apply.

Logit or similar

Diversion curve

Factors


Modeled directly in demand model (no factors)

Factors or percentages applied to trip tables before assignment


IV.9 Please describe the model structure. Please check (✓) one box only.

Traditional four-step (generation, distribution, modal split, assignment)

Trip assignment only (no demand modeling, or demand is forecast externally to themodel)

Activity-based modeling


IV.10 Does the model have feedback loops? Please check (✓

) one box only.

Yes—assignment impedances are fed back to distribution and/or modal split for____ cycles

Yes—other ( please describe):

No feedback loops

IV.8 Please indicate how the peak hour is derived. Please check (✓) one box only.



73

IV.15 Please describe the tolling costs that are modeled.

Value of time: [please check (✓) all boxes that apply]


Willingness to pay: [please check (✓) all boxes that apply]



Willingness to pay: the value of time that accounts for how much travelers value different attributes of theproposed facility, as opposed to its mere availability. Typically, willingness to pay is greater than the valueof time. Reasons that have been put forward to explain the higher value include the assumed safety andconvenience that the new facilities would provide.

IV.16 Are variable tolls modeled? Please check (✓) one box only.

Yes

No

IV.17 How are tolls taken into account in trip assignment (route choice)? Please check (✓) allboxes that apply.

No modal split model

IV.13 Please describe the formulation of the trip assignment model (methods for route choicemodeling). Please check (✓) one box only.

Equilibrium assignment

All-or-nothing

Capacity restraint


IV.14 Please describe the methods used for time choice modeling. Please check (✓) all boxesthat apply.

Peak spreading model

Time choice model

Arrival/departure time choice model

Factors (e.g., peak to 24-hour factors) from surveys, traffic counts, or other sources


No time-of-day choice models



74

IV.20 What data were used to calibrate or validate your model? Please check (✓) all boxes thatapply.


Origin–destination survey

Activity- or tour-based survey

Stated preference survey

Traffic counts

Speed/travel time surveys

Land use inputs

Network characteristics

Other ( please explain): ____________________________

IV.21 What tests were used to validate the base year model assignment results? Please check (✓) all boxes that apply.

Generalized cost in volume-delay function

Diversion curves


IV.18 Was this model calibrated specifically for this analysis? Or was it adapted from anothermodel? Please check (✓) all boxes that apply.

New model was calibrated specifically for this toll demand and revenue forecast

Model was updated/enhanced from an existing model that was calibrated forprevious toll demand and revenue forecasting studies

Model was updated/enhanced from an existing model that was calibrated for otherpurposes (e.g., LRTP, TIPs, etc.)

Existing model was used as is without special adaptations


Don’t know

IV.19 Are there any special or innovative features related to this application, not otherwisedescribed above?



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IV.23 Were changes, corrections, or improvements recommended for the model or the forecastsas a result of the verification?

Yes ( please explain):

No

IV.24 Were these recommendations implemented?

Yes—all

Yes—some ( please identify):

No—not implemented in current model or forecasts

Planned for implementation in future model and forecasts

IV.25 Have the results of your forecasts been accepted by the intended audience/decisionmakers?

Yes, unconditionally

Yes, with qualifications or conditions ( please explain):

Comparison of ratios of simulated and observed volumes at screenlines, cutlines,cordons, etc.

Statistical tests (R2, RSME, GEH, etc.) comparing simulated and observed link volumes

Comparison of simulated and observed travel times or speeds on links, facilities,

corridors, etc.


No tests/don’t know

IV.22 Please indicate how your model and/or toll road forecasts have been verified. Pleasecheck (✓) all boxes that apply.

None/not done (→ please proceed to Part III )

Judgment/reality check (e.g., comparison with older forecasts)

Statistical verification

Risk assessment

Sensitivity tests of key parameters or inputs

Use of ranges of forecasts

Independent audit/critical review




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Ramp-up forecasts versus

actual conditions




Medium-term (5–10 years)forecasts versus actualconditions (if available to theapplication you aredescribing)




Long-term (>10 years)forecasts versus actualconditions (if available to the

application you aredescribing)




V.2 What impact did the differences (if any) identified in question III.1 have on the forecastsor on the use of the forecasts? Please check (✓) all boxes that apply.

Model was recalibrated/model networks were reconfigured

No ( please explain):

IV.26 Were network micro-simulation models used in the development of demand and revenueforecasts (e.g., for a HOT lane)?

Yes

No

IV.27 What other type(s) of models were used for this application? Please check (✓) all boxesthat apply.

Land use/economic forecasting model (what software?) ___

Traffic operations model (what software?)


None

Part V. Experience with toll road demand and revenue forecasts

The questions in Part V refer to the application you described in Part IV.

V.1 Please describe how your toll road demand forecasts have compared with actual demandconditions or with the expected performance of your facility.



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Environmental or economic development considerations

Modeling:

Model structure

Process of expanding modeled time periods to annual forecasts

Transparency/opacity in the modeling and forecasting processes

Calibration process, coverage, and precision

Some modes were not modeled (e.g., trucks) or were not modeled well

“Control” over how the model outputs were used, analyzed, or interpreted

Operations:

Staging of proposed toll facility (or other facilities)

Actual operations and system reliability (e.g., congestion levels, operatingspeeds, frequency of incidents, etc.)

Impact of tolling technology on actual traffic volumes (e.g., unreadable licenseplates)

Demand forecasts were revised

Revenue forecasts were revised

Financing schedule was revised

Performance indicators changed/new indicators implemented

Tolling structure or rates were changed

Project costs changed

Staging or timing of project was revised

Project postponed or cancelled

Policies revised/new policies adopted


No impact (forecasts accepted and used as is)

V.3 What factors influenced the performance of the forecasts identified in Question III.1?Please check (✓) all boxes that apply.

Inputs:

Assumptions regarding land use or future base network configurations onparallel/competing routes or modes

Availability, appropriateness, or sufficiency of data, models, or analyticalcapabilities

Values used for value of time/willingness to pay and/or other monetary values

Public and political inputs regarding land use and network assumptions



78


V.5 What recommendations from previous forecasting applications did this model (for thisspecific application) already incorporate? Please check (✓) all boxes that apply.

Improved transparency in the modeling and forecasting processes (so that decisionmakers were better informed)

Improved methods for travel demand forecasting modeling

Collected better or more data as the basis for model calibration or to monitorconditions

Provided better training for modeling and planning staff

Found better ways to tie modeling process to organizational/facility business orfinancing plan

Conducted more risk assessments in forecasts

Conducted more critical reviews and audits

Violation rate

Changes in policy, mandate, legislation, ownership, political environment, etc.



V.4 What recommendations do you have to improve the usability, accuracy, reliability, andcredibility of travel demand models and forecasts for estimating toll revenues and forfinancing? Please check (✓) all boxes that apply.

Improve transparency in the modeling and forecasting processes (so that decision

makers are better informed) Improve methods for travel demand forecasting modeling








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Other priorities


None

Are there any other comments that you would like to make, either on topics that have notbeen addressed earlier or to amplify or clarify what you have already said?


V.6 Which of these recommendations do you plan to implement in your models or in yournext application? Please check (✓) all boxes that apply.







Conduct more critical reviews and audits Other ( please describe):

V.7 What factors would prevent you from implementing these improvements? Please check (✓) all boxes that apply

Lack of financial resources

Lack of staff

Lack of time

Don’t know how to go about it

Not in our mandate



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Reminder: To support our research, we seek copies of relevant reports: For example, reports

and documents that describe your travel demand forecasting model, its calibration, the underlyingdata upon which the model is based, the tolling forecasts for which it has been used, reviews andaudits of the model and/or forecasts, and any other information that you see as being relevant.

The survey is now complete.

Please submit the survey by fax toll free at 1-888-618-4981 or by mail at the followingaddress:

iTRANS Consulting Inc.100 York Boulevard, Suite 300

Richmond Hill, ON L4B 1J8 Canada



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APPENDIX C

Summary of Survey Responses

Part I. Background information

I.1 What is your organization’s interest and/or mandate in toll road facilities? Please check (✓) one box only.

Responses*

We are currently or plan to be an owner/operator 21

Bond rater/insurer 2

Travel forecasting or other consultant/independent reviewer 0

None planned or expected 19

Other (explanation below): 3

*Note: There were also 10 agencies who responded by e-mail citing various reasons why they were unable to completethe survey at the time it was sent. A total of 45 agencies submitted the survey.

Comments related to the three respondents who selected “Other”:

1. [A state DOT] is planning a feasibility study

2. [A responding DOT] proposed merger with [a tolling authority]

3. [A state DOT] is working to determine the feasibility of tolling, but there is not have enough detail toanswer these questions

I.2 What types of tolled facilities does your organization currently or plan to own or operate?Please check (✓) all boxes that apply.

Facility Type Currently OwnCurrentlyOperate

Plan to OwnPlan toOperate

Urban expressway 8 7 11 11

Rural or intercity expressway 2 2 6 5

HOT lane 0 0 2 2

Express toll lane 2 1 1 1

Bridge or tunnel 8 8 6 4

Urban arterials 0 0 2 2

Rural arterial highways 1 1 2 2




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1. Currently Own—Toll supported bridges

2. Not Stated —[A tolling authority] currently operates 90 centerline-meters

3. Not Stated —3 toll bridges, including 2 at the border

I.3 What tolling technologies are used or are planned? Please check (✓) all boxes that apply.

Tolling Technologies Currently Use Plan to Use

Traditional toll plaza (manual toll collection only) 5 1

Toll plaza with combination of manual and electronic tollcollection (EZPass, FasTrak, etc.)

13 10

Fully electronic toll collection (transponder, record license plate) 2 7


Comments related to the four respondents who selected “Other”:

1. Not Stated —Study only at this time

2. Not Stated—We also have coin machines

3. Not Stated—High-speed electronic toll collection lanes are used

4. Plan to Use—Open road tolling

I.4 What sources of financing were used or are planned for the construction of these toll roadfacilities? Please check (✓) all boxes that apply.

Source of Financing Currently Use Plan to Use

Federal government (all sources) 7 9

State government 7 11

Local/county/district government 4 5

Public–private partnership 4 7

Private sector 1 0

Bond financing 15 11


Comments related to the eight respondents who selected “Other”:

1. Plan to Use—Any funding sources will be explored

2. Currently Used—Development impact fees

3. Not Stated—State funds for some [rights-of-way] and [for maintenance]

4. Not Stated—Project conceived as public–private partnership, but rejected in favor of bonds



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6. Currently Used—Toll revenue

7. Plan to Use—Development impact fees

8. Plan to Use—Blank

I.5 For what purpose(s) are the generated revenues used or planned? Please check (✓) allboxes that apply.

Use of Generated Revenues Currently Use Plan to Use

Facility construction 10 11

Facility operation and maintenance 15 13

Expansion of existing facility 9 8

Other roads or highways 2 4

Public transit 2 2

Debt service on bonds 14 12

General tax revenues (not specific to the facility) 1 1

Other (explanation below): 1 0


1. Currently Used—Operations and investment for other [a tolling authority] facilities through consolidatedbonds

2. Not Stated—Bond proceeds other roads and open space

3. Not Stated—Public partnership projects

I.6 What are the current or planned toll structure and rates for your toll roads?

Toll Structures Currently Use Plan to Use

Point toll (e.g., expressed as $/trip) 11 6

Distance toll—fixed (e.g., expressed as $/mile) 3 3

Distance toll—variable (e.g., expressed as $/mile) 2 4



1. Not Stated—[A state DOT] has no toll facilities

2. Not Stated—Per axle toll

3. Not Stated—Probably a mixture

4. Not Stated—Variable structure

5. Currently Used—Excess revenues



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6. Not Stated—Under study at this time

7. Not Stated—Too early in development stage to determine

8. Currently Used—Time of day and class of vehicle

I.7 What is your organization’s role in or use of toll road demand and revenue forecasts?Please check (✓) all boxes that apply.

Responses

Prepares travel demand (traffic) models/forecasts 8

Prepares or collects the data that are used for the travel demand models/forecasts 9

Conducts peer reviews or critical reviews 5

Uses the travel demand forecasts to prepare revenue forecasts or conduct a financial analysis 14

Uses the travel demand forecasts to approve or ensure funding 10


Comments related to the seven respondents who selected “Other”:

1. Manage and review consulting studies

2. Revenue forecast and bond financing

3. Policy and master plans

4. Procures travel demand and finance analyst consultant services

5. Use traffic and revenue studies to establish financial feasibility of proposal project

6. Sell bond in the financial market

7. Revenue forecasts are based on historical receipts

I.8 If your organization does not prepare its own toll road demand forecasts internally, whoprovides the forecasts for your use? Please check (✓) all boxes that apply.

Responses

Consultant 13

Regional MPO/COG 2

State DOT 1



1. A consultant will do eventually

5. Not Stated—Barrier system ~ 0.1 per mile



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Part II. State of practice in travel demand forecasting models for toll


II.1 Please describe the specific application for which the model was (or is to be) used.

The following information was needed to help us ensure that our survey has covered abroad range of perspectives. We also needed to follow-up with respondents for clarifications of responses. In the survey the respondent was assured that the answers to this question would bekept strictly confidential and would not be disclosed to outside parties. Therefore no responsesare provided for this question.

II.2 To what type of analysis was the model applied? Please check (✓) all boxes that apply.

Responses

Conceptual plan/feasibility plan 3

Policy study 2

Alternate analysis 6

Investment grade 6

Design forecast 3

Critical review or audit 2

Risk assessment analysis 1


Comments related to the six respondents who selected “Other”:

1. Operating budget

2. State environmental analysis

3. RTP/ITP

4. Operating revenue forecast

5. Project year 2005 yearly volumes at the seven toll plazas

6.

Note: RTP/ITP = regional transportation plan/intermodal transportation plan.

Toll revenue and demand forecasting for existing facilities

2. Not yet determined; likely a consultant

3. N/A



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Response #1

MINUTP

6-county [state]

metro region ~ 2.5 million 2000

Response #2 TranPlan 5.3 million 2000

Response #3 Transcoreproprietary and

Excel

Vehicles per minute

Response #4TranPlan 4,700 square miles 1.8 million

1997 calibratedannually for [a

tolling authority]

Response #5Cube Voyager 3,968 square miles 3.8 million

2000 calibration/ 2005 validation

Response #6 Microsoft Excel N/A N/A 2004Response #7 TranPlan 6,288 square miles 3.2 million 2001

Response #8 TranPlan 65 square miles 9.2 million 2003

Response #9Microsoft Excel

[Two] countiesbordering toll plaza

1998–2004

Response #10 Econometric modelsestimated in reviewsand operationalizedin Microsoft Excel

1,500 square miles 18 million2003 (updated

annually)

Response #11 EMME/2 2,750 square miles 4,306,700 1995

Response #12 EMME/2 9,000 squarekilometers (3,745square miles)

5.5 million 1996

Response #13 EMME/2 4,200 square miles 2.5 million 2000

II.4 Please describe the characteristics of your model’s networks and zone system.

No. of Traffic Zones No. of Traffic Links No. of Network Nodes

Response #1 437 [No response] [No response]

Response #2 3,043 55,000 20,000

Response #3 1 7 7

Response #4 2,036 27,501 12,575

Response #5 1,740 40,000 30,000

Response #6 0 1 0

Response #7 953 17,400 6,300

Response #8 3,378 35,000 25,000

II.3 Please describe the following basic characteristics of your travel demand forecastingmodel. Please answer all questions and/or check (✓) all boxes that apply.

Software Used forthe Model

Area Covered by theModel

Population Coveredby the Model

Base Year of Calibration



87

Response #12 1,700 40,000 12,000

Response #13 2,600 10,309 18,836

II.5 Please describe the types of links (tolled and not tolled) in your modelís networks. Pleasecheck (✓) all boxes that apply.

Type of Links Not Tolled Tolled

Expressways 9 10

Arterial roads 9 1

Collector roads 9 0

Local roads 7 0

Transit networks 3 0



1. Not Stated—HOV

2. Tolled—Bridge

3. Not Stated—[State] 400 only tolled facility

4. N/A

5. Tolled—Bridge tunnel

6. Tolled—Bridges and tunnels

7. Not Tolled—Interstate (limited access)

II.6 Please describe the modes that are modeled. Please check (✓) all boxes that apply.

Responses

Single-occupancy passenger vehicle (SOV) 9

High-occupancy passenger vehicle (HOV) 7

Vehicles in commercial use (e.g., repair vans, taxis, courier trucks, etc.) 4

Trucks (light and heavy) 8

Emergency/military vehicles 0

Buses 5

Passenger rail 3

Response #9 No formal model No formal model No formal model

Response #10 [No response] 8 toll plazas [No response]

Response #11 986 61,000 one-way 15,000



88

2. All vehicle types listed above are forecasted, but distinct models are used to project revenue-payingautos, buses, light trucks, and heavy trucks.

3. Passenger cars with no specified vehicle occupancy

II.7 Please describe the time periods modeled. Please check (✓) all boxes that apply.

Responses

Weekday AM peak hour 9

Weekday PM peak hour 8

Weekday other peak 2Weekday off-peak 8

Weekday 24 hour 5

Weekend 2



1. Weekday night period

2. Peak season weekday

3. Aggregate monthly

4. Entire 365 day year

5. All time periods broken out for each vehicle-type forecast; however, distinct models by time periodare factored by relevant activity data to arrive at time-of-day forecasts.

6. Midday (9 a.m.–3 p.m.) and nighttime (6 p.m.–6 a.m.)

7. Nighttime (NT)

II.8 Please indicate how the peak hour is derived. Please check (✓) one box only.

Responses

Modeled directly in demand model (no factors) 3

Factors or percentages applied to trip tables before assignment 4


Non-response 1



1. Airport



89

4. Provided by MPO

5. Percentage applied to peak period to get peak hour

II.9 Please describe the model structure. Please check (✓) one box only.

Responses

Traditional four-step (generation, distribution, modal split, assignment) 4

Trip assignment only (no demand modeling, or demand is forecast externally to the model) 3*

Activity-based modeling 1

Other (explanation below): 5Non-response 0

*Note: One respondent to this option (trip assignment only) commented that the “MPO provides trip tables obtainedfrom the regional model.”

Comments related to the five respondents who selected “Other”:

1. Traditional four-step plus toll diversion

2. Projection

3. Existing volumes, growth rate, estimated increase/decrease due to specific development or detour

4. Econometric model for traffic projection by vehicle type and facility type

5. Non-traditional four-step with toll trips as mode choice

II.10 Does the model have feedback loops? Please check (✓) one box only.

Responses

Yes—assignment impedances are fed back to distribution and/or modal split for cycles 6*

Yes—other 0

No feedback loops 7

Non-response 0

*Note: Two respondents that selected the first option commented as follows: (1) “1 cycle for mode share only,” and (2)“Yes—other→Mode Choice.”


1. Peak hour not modeled

2. Factors applied to daily assignment

3. N/A



90

Shopping 7

Leisure/recreation 6

Personal business (e.g., medical appointment) 5

Serve passenger (pick up/drop off) 2

Commercial vehicles (i.e., trucks, etc.) 2



1. Home-base other, non-home-base, air [passengers]

2. Home-base other/non-home-base other

3. Major tourist centers are special generators

4. N/A

5. Work/business-related and non-work/other

6. None

7. All trip purposes are modeled by vehicle type (auto, bus, light truck, heavy truck) for each crossing

8. Non-home-based non-work (NHBNW), home-base other (HBO)

II.12 Please describe the formulation of the modal split model (methods for mode choicemodeling). Please check (✓) all boxes that apply.

Responses

Logit or similar 6

Diversion curve 0

Factors 4

No modal split model 3*Other (explanation below): 3

*Note: One respondent to the fourth option (no modal split model) commented that “Assignment based on vehicle triptables.”

Comments related to the 3 respondents who selected “Other”:

II.11 Please describe the trip purposes modeled. Please check (✓) all boxes that apply.

Responses

Work (commute) 7

Work-related 7

Out-of-town business 2

School/education 6



91

Responses

Equilibrium assignment 8

All-or-nothing 1

Capacity restraint 1

Other 2

Non-response 1

Comments related to the two respondents who selected “Other”:

1. N/A

2. Capacity and time-value of motorists to select nearby toll bridge or further free bridge

II.14 Please describe the methods used for time choice modeling. Please check (✓) all boxesthat apply.

Responses

Peak spreading model 1

Time choice model 1

Arrival/departure time choice model 0

Factors from surveys, counts, or other sources 6

No time-of-day choice models 4



1. N/A

2. Mode choice is run by the MPO

II.15 Please describe the tolling costs that are modeled.

Value of Time* Willingness to Pay* Other*

Mode 4 0 0

1. N/A

2. Trip tables provided by MPO

3. Revealed preference (origin–destination) survey

II.13 Please describe the formulation of the trip assignment model (methods for route choicemodeling). Please check (✓) one box only.



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2. Vehicle Class—By vehicle class. Toll and fuel prices are controlled for in the econometric model.

3. Purpose—Travel time, travel cost, and income modeled as mode choice utility equations classified bytrip purpose and time of day.

II.16 Are variable tolls modeled? Please check (✓) one box only.

Responses

Yes 3

No 10

Non-response 0

II.17 How are tolls taken into account in trip assignment (route choice)? Please check (✓) allboxes that apply.

Responses

Generalized cost in volume-delay function 4

Diversion curve 5



1. N/A

2. Generally following existing automobile choice from existing data

3. Toll elasticities are estimated from observed response to toll changes and variable pricing.

II.18 Was this model calibrated specifically for this analysis? Or was it adapted from anothermodel? Please check (✓) all boxes that apply.

Responses

New model was calibrated specifically for this toll demand and revenue forecast 2

Vehicle class 4 2 2

Purpose 4 2 1

*Note: One respondent who did not select any option commented “N/A.”


1. Vehicle Class—Automobiles have choice to use lightweight bridge in some cases. Trucks generallydo not have a nearby alternative.



93

1. NTTM model is supported by information provided by the MPO

II.19 Are there any special or innovative features related to this application, not otherwise

described above?Response #1 Incorporated toll diversion curves into previously calibrated model

Response #2 The [state] toll facilities model is used to address queuing and delay at plazas

Response #3 Toll diversion model

Response #4 Trip reduction/consolidation factors due to tolling existing free bridge crossing

Response #5 The model is updated periodically to account for the behavioral changes in the [state]metropolitan area.

Response #6 This is the sixth annual projection completed. After the end of the year, actual toll volumeis compared with projected toll volume revenue for the year, and growth rates are adjustedaccordingly.

Response #7 The model allows scenario development/sensitivity testing of economic variables andassumptions regarding electronic payment utilization. A separate value pricing model hasbeen developed that allows for estimation of traffic/revenue impacts of pricing changes byvehicle class, hour, method of payment, and crossing.

Response #8 A major shortcoming of a planning-level regional model is that traffic operations on eachlink operate independently of every other link. That is, there is a certain level of delayassociated with a link that results from the traffic volume and link capacity only. Thereality of external influences such as traffic from merging roadways or downstreamblockages are not reflected in the regional model since queuing impacts from thesesituations are an important component of travel delay along the expressway during peak hours, it was necessary to modify link capacities in those areas where queue buildup issignificant.

Response #9 Auto trips split into toll and non-toll components by trip purpose and time of day in themode choice step

Note: Nine of 13 respondents to Part II answered this question.

II.20 What data were used to calibrate or validate your model? Please check (✓) all boxes thatapply.

Model was updated/enhanced from an existing model that was calibrated for other purposes(e.g., LRTP, TIPs, etc.)

6*

Existing model was used as is without special adaptations 1

Don't know 0


*Note: One agency that selected the third option commented “[Toll Authority] revalidates annually.”

Comments related to the one respondent who selected “Other”:

Model was updated/enhanced from an existing model that was calibrated for previous toll

demand and revenue forecasting

4



94


1. Calibration—Electronic toll collection perception

2. Calibration—Characteristic of toll users3. Validation—Toll impedance

4. Validation—Electronic toll collection perception

5. Validation—Characteristic of toll users

II.21 What tests were used to validate the base year model assignment results? Please check (✓) all boxes that apply.

Responses

Comparison of ratios of simulated and observed volumes at screenlines, cutlines, cordons, etc. 10

Statistical tests (R sq., RSME, GEH, etc.) 7

Comparison of simulated and observed travel times or speeds on links, facilities, corridors, etc. 8

No tests/don’t know 2



1. K factors adjusted to reflect origin–destination

2. Compared actual vs. projected volumes from previous study

3. Sensitivity analysis

II.22 Please indicate how your model and/or toll road forecasts have been verified. Pleasecheck (✓) all boxes that apply.

Responses

None/not done 5*

Origin–destination survey 5 7

Activity- or tour-based survey 2 1

Stated preference survey 3 3

Traffic counts 8 10

Speed/travel time surveys 3 9

Land use inputs 2 6

Network characteristics 3 6

Other (explanation below): 2 3




95

1. Validated against existing conditions

2. Observed traffic

II.23 Were changes, corrections, or improvements recommended for the model or the forecastsas a result of the verification?

Responses

Yes (explanation below) 5

No 3

Non-response 5

Comments related to the five respondents who selected “Yes”:

1. [No response]

2. The model is updated periodically

3. Econometric models were respecified when realistic out-year forecast growth rates were not achievedby vehicle type. Quarterly forecasts are now being developed to refine annual forecast errors andimprove on variance reporting throughout the year.

4. [No response]

5. Model refinements in validation

II.24 Were these recommendations implemented?

Responses

Yes—All 4

Yes—Some (explanation below) 1

No—Not implemented in current model forecasts 0

Planned for implementation in future models and forecasts 0

Judgment/reality check 8*

Statistical verification 2

Risk assessment 2

Sensitivity tests of key parameters or inputs 7*

Use of ranges of forecasts 1

Independent audit/critical review 2


*Note: One respondent to the first, (none), second (judgment), and fifth (sensitivity tests) commented that “Tolling notin place yet.”




96

No (explain) 0

Non-response 5

*Note: One respondent to the first option commented that “Bonds have been rated on 3 occasions.”

II.26 Were network micro-simulation models used in the development of demand and revenueforecasts (e.g., for a HOT lane)?

Responses

Yes 0

No 8

Non-response 5

II.27 What other type(s) of models were used for this application? Please check (✓) all boxesthat apply.

Responses

Land use/economic forecasting model (software used listed below) 2

Traffic operations model (software used listed below) 1


None 1

Comments related to the six respondents who selected “Land Use/Economic Forecasting Model” or“Traffic Operations Model” or “Other”:

1. Land use/economic forecasting model—Proprietary

2. Traffic operations model—HCM for the environmental studies

3. Land use/economic forecasting model—DRAM/EMPAL

4. Other—Independent economic review

Non-response 8

Comments related to the one respondent who selected “Yes—Some”:

1. Additional data are needed for high-speed electronic toll collection

II.25 Have the results of your forecasts been accepted by the intended audience/decisionmakers?

Responses

Yes, unconditionally 7*

Yes, with qualification of conditions 1



97

conditions

Long-term (>10 years) forecasts vs. actualconditions

0 0 2

Corresponding ramp-up percentage 50% — —

Corresponding medium-term percentage — 24% —

Corresponding long-term percentage — — —

III.2 What impact did the differences (if any) identified in Question III.1 have on the forecastsor on the use of the forecasts? Please check (✓) all boxes that apply.

Responses

Model was recalibrated/model networks were reconfigured 2

Demand forecasts were revised 2

Revenue forecasts were revised 2

Financial schedule was revised 1

Performance indicators changed/new indicators implemented 0

Tolling structures or rates were changed 0

Project costs changed 0

Staging or timing of project was revised 1

Project postponed or canceled 1

Policies revised/new policies adopted 0

No impact (forecasts accepted and used as is) 7*


*Note: One respondent to the eleventh option (no impact) commented “Facility not tolled yet.”


5. Other—National/regional economic forecast variables from Global Insight and Economy.com

6. Other—A logit modeling software

Part III. Experience with toll road demand and revenue forecasts

III.1 Please describe how your toll road demand forecasts have compared with actual demandconditions or with the expected performance of your facility.

Traffic ForecastsOverstated

Traffic ForecastsUnderstated

Traffic Forecastswithin 5%

Ramp-up forecasts vs. actual conditions 1 0 7

Medium-term (5–10 years) forecasts vs. actual 0 1 4



98

Public and political inputs regarding land use and network assumptions 2

Environmental or economic development considerations 2



1. Economic climate

Modeling:

Responses

Model structure 4

Process or expanding modeled time periods to annual forecasts 4

Transparency/opacity in the modeling and forecasting processes 1

Calibration process, coverage, and precision 2

Some modes were not modeled (e.g., trucks) or were not modeled well 0

“Control” over how the model outputs were used, analyzed, or interpreted 2



1. Validity of models for financing purposes

Operations:

Responses

Staging or proposed facility (or other facilities) 2

1. Annual updates and peer reviews

2. Anticipated revenues are used to determine upcoming revenues. If revenues are overstated, someprojects may be postponed.

III.3 What factors influenced the performance of the forecasts identified in Question III.1?Please check (✓) all boxes that apply.

Inputs:

Responses

Assumptions regarding land use or future base network configurations on parallel/competingroutes or modes

5

Availability, appropriateness, or sufficiency of data, models, or analytical capabilities 4

Values used for value of time/willingness to pay and/or other monetary values 6



99

bottom of the inputs table)

Note: Two of 13 respondents to Part III answered this question.

III.4 What recommendations do you have to improve the usability, accuracy, reliability, andcredibility of travel demand models and forecasts for estimating toll revenues and forfinancing? Please check (✓) all boxes that apply.

Responses

Improve transparency in the modeling and forecasting processes (so that decision makers arebetter informed)

4

Improve methods for travel demand forecasting modeling 8

Collect better or more data as the basis for model calibration or to monitor conditions 8

Provide better training for modeling and planning staff 5

Find better ways to tie modeling process to organizational/facility business or financing plan 3

Conduct more risk assessments in forecasts 7

Conduct more critical reviews and audits 4



1. More direct relationship to economic factors that determine travel demand

III.5 What recommendations from previous forecasting applications did this model (for thisspecific application) already incorporate? Please check (✓) all boxes that apply.

Actual operations and system reliability (e.g., congestion levels, operating speeds, frequency of incidents, etc.)

4

Impact of tolling technology on actual traffic volumes (e.g., unreadable license plates) 4

Violation rate 3

Changes in policy, mandate, legislation, ownership, political environment, etc. 1

Other 0


Response #1 Consideration of the factors above were among the reasons the [state toll road] forecast hasbeen successful [factors checked included inputs table (first three responses reading fromtop to bottom), modeling table (the first four and the six responses), operations table (firstfour responses)].

Response #2 Not a standard link–node model (only check the 1 and 3 responses reading from top to



100

III.6 Which of these recommendations do you plan to implement in your models or in yournext application? Please check (✓) all boxes that apply.

Responses

Improve transparency in the modeling and forecasting processes (so that decision makers werebetter informed)

1


Collect better or more data as the basis for model calibration or to monitor conditions 7Provide better training for modeling and planning staff 1




Other 0

III.7 What factors would prevent you from implementing these improvements? Please check (✓) all boxes that apply

Responses

Lack of financial resources 5

Lack of staff 1

Lack of time 4

Don’t know how to go about it 0

Not in our mandate 1

Other priorities 5


Collect better or more data as the basis for model calibration or to monitor conditions 6Provide better training for modeling and planning staff 2






1. Simplicity

2. More direct relationship to economic factors that determine travel demand

Responses

Improve transparency in the modeling and forecasting processes (so that decision makers were

better informed)

3



101

Response #1 The adaptation of the MPO models for use by this agency has been refined over a 10-yearperiod. The forecasts results are review monthly against actual reviews. This applicationhas been brought before the rating agencies on numerous occasions and forecast reviewhave not been a bond rating issue as the forecasts are consistently 2%–5% lower thanactual. The agency recently received a rating agency upgrade on the strength of the lastbond presentation and the appropriately conservative nature of the revenue forecasts. Theprocess of forecasting revenues benefits from a built-in peer review process and rigorousannual updates used for the traffic engineers annual revenue forecasts. These forecasts areused to set the agencies annual operating budget. Every year, a specific aspect of themodel is focused on. Examples include sub-area land use updates, new data on high-speedelectronic toll collection performance, or updates to remain consistent with the regionaltransportation models.

Response #2 [A tolling authority] does not employ a computer-based travel demand and forecasting

model. Rather, we perform manual projections and analysis to support our currentoperations and possible changes to toll structure.

Response #3 Most of this survey does not apply directly to [bond rating agency], and we haveexperience working with forecasts generated by too many different models to address theproblems we have had with each of them individually, but I think that most of therecommendations in this survey would be helpful for most if not all of the forecasts wehave worked with, especially for start-ups.

Response #4 Part II asks for details on “state-of-the-practice” travel demand forecasting models. Part IIIasks for ramp-up, medium and long-term model results of the same model described in PartII. These parts are in conflict. Traffic and revenue forecasts leading to project decisionsare prepared years before those projects are chosen, designed, and constructed. Therequired lead time is easily 10 years. So to be able to answer Part III, the survey asks us to

pick a model application that is at least 10 years and most likely +20 years old. Models of such lineage cannot be considered as “state-of-the-practice” by any stretch of theimagination. We have chosen to respond to Part II with what we consider to be our mostadvanced model development (i.e., our state-of-the-practice) achievements. The traffic andrevenue forecasts produced in this manner are for projects that have no traffic historysimply because they are still in production. Therefore, we are unable to providecorresponding “state-of-the-practice” responses for Part III.

Note: Four of 13 respondents to Part III answered this question.

None 2

Other 0

III.8 Are there other models that were used for other applications that you would like to

describe?

Responses

Yes 0

No 10

Non-response 3

Are there any other comments that you would like to make, either on topics that have not beenaddressed earlier or to amplify or clarify what you have already said?



Response #9 [State] toll roads were built in the 1950s, 60s, and 70s. Of the 10 constructed, tolls remainonly on 2. There have been discussions involving the future use of tolls on existingfacilities and also on project tolls; however, no detailed analysis has been undertaken at thistime.

Response #10 [State] is planning on owning/operating a toll facility with [a state DOT] when they eitherget funds to complete their portion or when state law allows them to own/operate a tollfacility.

Other comments were made by respondents who only completed Part I or Part II of the survey.These are documented here:

Response #5 [State DOT] is presently in the process of selecting consultants to perform toll feasibilitystudies for three locations. We do not now have any toll facilities owned/operated by the

state.

Response #6 We are new. We would like a copy of the results when complete if possible.

Response #7 [A state DOT] has only three toll facilities: [three bridges]. Our statewide travel demandmodel has not recently been used to forecast traffic over these crossings. Traffic forecastsfor future studies at proposed new border crossings will probably be the subject of specialcontracts, and will probably not be sensitive to the impact of toll rates, since there are nountolled alternatives to the crossings: Our statewide model contains no provision for testingtoll alternatives or making toll-road forecasts, other than to change the impedance on linksproposed as toll routes. It has never been used for this purpose. No toll-road projects areunder study in Michigan, and none are foreseen.

Response #8 No consultant performing toll forecasting at this stage of the project

102



103

APPENDIX D

Characteristics of Recorded Toll Roads

Authority/Tollway Description


Sawgrass Expressway• New 23-mile outer loop road in Broward County with an

original toll per mile rate of 6.5 cents. There was no

projected toll increase (2).

North Texas Tollway

Authority/Dallas North

Tollway

• 5.1-mile extension to existing 9.8-mile tollway in

Dallas/Colin counties with an original toll per mile charge

rate of 7.1 cents. There was no projected toll increase (2).

Harris County Toll Road

Authority/Hardy

• New 21.7-mile road with an original toll per mile rate of 9.2

cents. There was no projected toll increase (2).

Harris County Toll Road

Authority/Sam Houston• New 27.5-mile outer loop road with an original toll per mile

rate of 7.6 cents. There was no projected toll increase (2).

Illinois State Toll Highway

Authority/Illinois North South

Tollway

• New 17-mile road in Dupage/Will counties with an original

toll per mile rate of 5.7 cents. There was no projected toll

increase (2).

Orlando–Orange Expressway

Authority/Central Florida

Greenway North Segment

• Initial 5.1-mile part of beltway in East Orlando with an

original toll per mile rate of 9.9 cents. There was a projected

toll increase in the second year (2). Toll rate reduced and new

beltway segment added in year 4, which were not in the

original forecast (1).

Orlando–Orange ExpresswayAuthority/Central Florida

Greenway South Segment

• Additional 7 miles of beltway in east Orlando with an originaltoll per mile rate of 7.1 cents. There was no projected toll

increase (2). New beltway segments added in years 4/5,

which were not part of the original forecast (1).


John Kilpatrick

• New 9.5-mile north partial outer loop in Oklahoma City with

an original toll per mile rate of 8.0 cents. There was no

projected toll increase (1).


Creek • New 7.0-mile southern bypass in Tulsa with an original toll

per mile rate of 7.2 cents. There was no projected toll

increase (1).

Mid-Bay Bridge Authority

(FL)/Choctawhatchee BayBridge

• Choctawatchee Bay Bridge connects the Niceville area in the

vicinity of White Point with Destin peninsula near PineyPoint, in Okaloosa County. The project was scheduled to

open in October of 1993, but opened 3 months early (14,15).

Orlando–Orange Expressway

Authority/Central Florida

Greenway Southern Connector

• 21-mile beltway in South Orlando with an original toll per

mile rate of 9.5 cents. There was no projected toll

increase (2).



104


State Road and Tollway

Authority (GA)/GA 400


Veteran’s Expressway

• New 15-mile expressway in northwest Hillsborough County

with an original toll per mile rate of 8.3 cents. There was aprojected toll increase in year 5; however, the proposed toll

increase was postponed (1).


Seminole

• 12-mile north segment of eastern Orlando beltway with an

original toll per mile rate of 12.5 cents. The proposed toll

increase in year 5 was postponed (1).

• New 6.2-mile road in north Atlanta/Fulton counties with an

original toll per mile rate of 7.8 cents. There was noprojected toll increase (1).

Transportation Corridor

Agencies (CA)/Foothill North

• 7.4-mile southern extension of existing Portola and Antonio

parkways in Orange County with an original toll per mile

rate of 13.3 cents. There was a projected toll increase in

year 7. A new beltway segment was added in years 4/5,

which was not part of the original forecast and therefore the

data from years 4/5 was marked as NR (nor reported) due toincompatibility (1).

Osceola County (FL)/OsceolaCounty Parkway

• Extends 12.4 miles east–west of the Florida turnpike fromOsceola to Reedy Creek (Disney World) with an original

toll per mile rate of 12.1 cents. There was a projected toll

increase in year 3 (1).

Toll Road Investment

Partnership (VA)/Dulles

Greenway

• 14 miles in Loudon County, Northern Virginia, near Dulles

Airport. An original toll per mile rate of 12.0 cents was

used and there was a projected toll increase in year 4: toll

rate increased in years 4/5 (1).

Transportation CorridorAgencies (CA)/San Joaquin

Hills

•

14 miles that travels Costa Mesa to San Juan Capistrano;bypass of El Toro Y on I-5. An original toll per mile rate of

13.8 cents was used and there was a projected toll increase

in year 4 (1).

North Texas Tollway

Authority/George Bush

Expressway

• 30.5 miles in the northern half of Dallas metropolitan area

with an original toll per mile rate of 10.7 cents. There was

no projected toll increase (1).

Transportation Corridor

Agencies (CA)/Foothill

Eastern

• 15-mile extension of existing Foothill North project and

extension of Route 91 near Riverside County, connecting I-5

and I-55. An original toll per mile rate of 18.0 to 21.7 cents

was used and there was a projected toll increase in year

3 (1).

E-470 Public Highway

Authority (CO)/E-470

• 46 miles in the eastern perimeter of Denver; short segment

coming off of I-25 in southeast Denver. An original toll per

mile rate of 14.0 cents was used and there was a projected

toll increase in year 3 (1).


Polk

• 25 mile partial loop around Lakeland/Winterhaven with an

original toll per mile rate of 11.1 cents. There was a

projected toll increase in year 5 (1).



105


Santa Rosa Bay Bridge

Authority (FL)/Garcon Point

Bridge

• 7.5 miles of roadway, plus 3.5 miles of bridge in Pensacola

for a total of 11 miles. An original toll per mile rate of 16.1

cents was used and there was a projected toll increase in

year 5 (1).

Connector 2000 Association

(SC)/Greenville Connector

• I-85 bypass extending 16 miles from I-85 to I-385 in

Greenville, South Carolina. The project opened early and

the year 1 data represents the first 9.5 months, annualized

for 2002. An original toll per mile rate of 9.5 cents was

used and there was a projected toll increase in year 3 (1).

Pocahontas Parkway

Association (VA)/Pocahontas

Parkway

• Route 895 Connector extends from the current eastern

terminus of Chippenham Parkway (SR-150) at I-95 to a

connection with I-295 southeast of Richmond International

Airport. It would include a major high-level bridge across

the James River and improved access to Richmond

International Airport and the overall I-64 corridor (18,19).

Northwest Parkway Public

Highway Authority (CO)/

Northwest Parkway

• The Northwest Parkway extends about 11 miles southwest

from the northern terminus of E-470 at I-25 to the

intersection of 96th Street and SH-128 in the Interlocken

area of Broomfield, Colorado (20,21).



Abbreviations used without definitions in TRB publications:

AAAE American Association of Airport ExecutivesAASHO American Association of State Highway OfficialsAASHTO American Association of State Highway and Transportation OfficialsACI–NA Airports Council International–North AmericaACRP Airport Cooperative Research ProgramADA Americans with Disabilities ActAPTA American Public Transportation AssociationASCE American Society of Civil Engineers

ASME American Society of Mechanical EngineersASTM American Society for Testing and MaterialsATA Air Transport AssociationATA American Trucking AssociationsCTAA Community Transportation Association of AmericaCTBSSP Commercial Truck and Bus Safety Synthesis ProgramDHS Department of Homeland SecurityDOE Department of EnergyEPA Environmental Protection AgencyFAA Federal Aviation AdministrationFHWA Federal Highway AdministrationFMCSA Federal Motor Carrier Safety AdministrationFRA Federal Railroad AdministrationFTA Federal Transit AdministrationIEEE Institute of Electrical and Electronics EngineersISTEA Intermodal Surface Transportation Efficiency Act of 1991ITE Institute of Transportation Engineers

NASA National Aeronautics and Space AdministrationNASAO National Association of State Aviation OfficialsNCFRP National Cooperative Freight Research ProgramNCHRP National Cooperative Highway Research ProgramNHTSA National Highway Traffic Safety AdministrationNTSB National Transportation Safety BoardSAE Society of Automotive EngineersSAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act:

A Legacy for Users (2005)TCRP Transit Cooperative Research ProgramTEA-21 Transportation Equity Act for the 21st Century (1998)TRB Transportation Research BoardTSA Transportation Security AdministrationU.S.DOT United States Department of Transportation

estimating toll road demand & revenue--trb, 2006

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